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Special Issue "Sensors for Transportation"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 September 2017)

Special Issue Editor

Guest Editor
Prof. Dr. Simon X. Yang

Advanced Robotics & Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
Website | E-Mail
Interests: electronic noses; smart sensors; sensor signal processing; multi-sensor fusion; sensor networks; robotics; intelligent systems; control systems; intelligent transportation; systems modeling and analysis

Special Issue Information

Dear Colleagues,

More and more sensors have been used in various aspects of transportation science and engineering. Sensor-based monitoring, operation, planning, control, and decision making has been the trend in air, water, and ground transportation systems, for safety, durability, and comfort, as well as environmental sustainability. Various traditional and newly-developed sensors have been used in various aspects of transportation systems for accurate information acquisition, effective monitoring, and optimal decision making.

This Special Issue is devoted to new advances and research results on sensor development, sensor signal processing, sensor based monitoring and decision making in theoretical, experimental and applied transportation systems. The topics in this Special Issue include, but are not limited to, sensor design and development for information acquisition in transportation, sensor signal processing in transportation, sensor placement and multi-sensor fusion for transportation, sensors based monitoring and operation, sensors based high-rise structures for transportation, sensor based control and decision making, modelling and analysis of sensors based transportation systems, sensor information processing and software development for transportation, and remote sensing and monitoring for transportation. The applications of various sensor technologies to transportation are also welcome.

Prof. Dr. Simon X. Yang
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sensor development and placement for transportation
  • Information acquisition and data analysis for transportation
  • Sensors-based planning and decision making in transportation
  • Sensors-based monitoring and control in transportation
  • Applications of sensors in transportation

Published Papers (53 papers)

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Research

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Open AccessArticle Temporal and Fine-Grained Pedestrian Action Recognition on Driving Recorder Database
Sensors 2018, 18(2), 627; https://doi.org/10.3390/s18020627
Received: 5 January 2018 / Revised: 7 February 2018 / Accepted: 8 February 2018 / Published: 20 February 2018
Cited by 3 | PDF Full-text (1485 KB) | HTML Full-text | XML Full-text
Abstract
The paper presents an emerging issue of fine-grained pedestrian action recognition that induces an advanced pre-crush safety to estimate a pedestrian intention in advance. The fine-grained pedestrian actions include visually slight differences (e.g., walking straight and crossing), which are difficult to distinguish from
[...] Read more.
The paper presents an emerging issue of fine-grained pedestrian action recognition that induces an advanced pre-crush safety to estimate a pedestrian intention in advance. The fine-grained pedestrian actions include visually slight differences (e.g., walking straight and crossing), which are difficult to distinguish from each other. It is believed that the fine-grained action recognition induces a pedestrian intention estimation for a helpful advanced driver-assistance systems (ADAS). The following difficulties have been studied to achieve a fine-grained and accurate pedestrian action recognition: (i) In order to analyze the fine-grained motion of a pedestrian appearance in the vehicle-mounted drive recorder, a method to describe subtle change of motion characteristics occurring in a short time is necessary; (ii) even when the background moves greatly due to the driving of the vehicle, it is necessary to detect changes in subtle motion of the pedestrian; (iii) the collection of large-scale fine-grained actions is very difficult, and therefore a relatively small database should be focused. We find out how to learn an effective recognition model with only a small-scale database. Here, we have thoroughly evaluated several types of configurations to explore an effective approach in fine-grained pedestrian action recognition without a large-scale database. Moreover, two different datasets have been collected in order to raise the issue. Finally, our proposal attained 91.01% on National Traffic Science and Environment Laboratory database (NTSEL) and 53.23% on the near-miss driving recorder database (NDRDB). The paper has improved +8.28% and +6.53% from baseline two-stream fusion convnets. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle An Improved Calibration Method for a Rotating 2D LIDAR System
Sensors 2018, 18(2), 497; https://doi.org/10.3390/s18020497
Received: 3 November 2017 / Revised: 26 January 2018 / Accepted: 30 January 2018 / Published: 7 February 2018
Cited by 2 | PDF Full-text (4291 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents an improved calibration method of a rotating two-dimensional light detection and ranging (R2D-LIDAR) system, which can obtain the 3D scanning map of the surroundings. The proposed R2D-LIDAR system, composed of a 2D LIDAR and a rotating unit, is pervasively used
[...] Read more.
This paper presents an improved calibration method of a rotating two-dimensional light detection and ranging (R2D-LIDAR) system, which can obtain the 3D scanning map of the surroundings. The proposed R2D-LIDAR system, composed of a 2D LIDAR and a rotating unit, is pervasively used in the field of robotics owing to its low cost and dense scanning data. Nevertheless, the R2D-LIDAR system must be calibrated before building the geometric model because there are assembled deviation and abrasion between the 2D LIDAR and the rotating unit. Hence, the calibration procedures should contain both the adjustment between the two devices and the bias of 2D LIDAR itself. The main purpose of this work is to resolve the 2D LIDAR bias issue with a flat plane based on the Levenberg–Marquardt (LM) algorithm. Experimental results for the calibration of the R2D-LIDAR system prove the reliability of this strategy to accurately estimate sensor offsets with the error range from −15 mm to 15 mm for the performance of capturing scans. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System
Sensors 2018, 18(2), 374; https://doi.org/10.3390/s18020374
Received: 30 November 2017 / Revised: 7 January 2018 / Accepted: 18 January 2018 / Published: 27 January 2018
Cited by 2 | PDF Full-text (4411 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the
[...] Read more.
This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the background using the adaptive Gaussian Mixture Model (GMM). After that, several geometric features are extracted, such as vehicle area, height, width, centroid, and bounding box. As occlusion is present, an algorithm was implemented to reduce it. The tracking is performed with adaptive Kalman filter. Finally, the selected geometric features: estimated area, height, and width are used by different classifiers in order to sort vehicles into three classes: small, midsize, and large. Extensive experimental results in eight real traffic videos with more than 4000 ground truth vehicles have shown that the improved system can run in real time under an occlusion index of 0.312 and classify vehicles with a global detection rate or recall, precision, and F-measure of up to 98.190%, and an F-measure of up to 99.051% for midsize vehicles. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model
Sensors 2018, 18(1), 298; https://doi.org/10.3390/s18010298
Received: 3 November 2017 / Revised: 17 December 2017 / Accepted: 15 January 2018 / Published: 19 January 2018
Cited by 2 | PDF Full-text (4195 KB) | HTML Full-text | XML Full-text
Abstract
Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become
[...] Read more.
Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Motorcycles that See: Multifocal Stereo Vision Sensor for Advanced Safety Systems in Tilting Vehicles
Sensors 2018, 18(1), 295; https://doi.org/10.3390/s18010295
Received: 31 October 2017 / Revised: 17 January 2018 / Accepted: 17 January 2018 / Published: 19 January 2018
Cited by 1 | PDF Full-text (16648 KB) | HTML Full-text | XML Full-text
Abstract
Advanced driver assistance systems, ADAS, have shown the possibility to anticipate crash accidents and effectively assist road users in critical traffic situations. This is not the case for motorcyclists, in fact ADAS for motorcycles are still barely developed. Our aim was to study
[...] Read more.
Advanced driver assistance systems, ADAS, have shown the possibility to anticipate crash accidents and effectively assist road users in critical traffic situations. This is not the case for motorcyclists, in fact ADAS for motorcycles are still barely developed. Our aim was to study a camera-based sensor for the application of preventive safety in tilting vehicles. We identified two road conflict situations for which automotive remote sensors installed in a tilting vehicle are likely to fail in the identification of critical obstacles. Accordingly, we set two experiments conducted in real traffic conditions to test our stereo vision sensor. Our promising results support the application of this type of sensors for advanced motorcycle safety applications. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle A Novel Segment-Based Approach for Improving Classification Performance of Transport Mode Detection
Sensors 2018, 18(1), 87; https://doi.org/10.3390/s18010087
Received: 30 November 2017 / Revised: 18 December 2017 / Accepted: 25 December 2017 / Published: 30 December 2017
Cited by 1 | PDF Full-text (1238 KB) | HTML Full-text | XML Full-text
Abstract
Transportation planning and solutions have an enormous impact on city life. To minimize the transport duration, urban planners should understand and elaborate the mobility of a city. Thus, researchers look toward monitoring people’s daily activities including transportation types and duration by taking advantage
[...] Read more.
Transportation planning and solutions have an enormous impact on city life. To minimize the transport duration, urban planners should understand and elaborate the mobility of a city. Thus, researchers look toward monitoring people’s daily activities including transportation types and duration by taking advantage of individual’s smartphones. This paper introduces a novel segment-based transport mode detection architecture in order to improve the results of traditional classification algorithms in the literature. The proposed post-processing algorithm, namely the Healing algorithm, aims to correct the misclassification results of machine learning-based solutions. Our real-life test results show that the Healing algorithm could achieve up to 40% improvement of the classification results. As a result, the implemented mobile application could predict eight classes including stationary, walking, car, bus, tram, train, metro and ferry with a success rate of 95% thanks to the proposed multi-tier architecture and Healing algorithm. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Anti-Runaway Prevention System with Wireless Sensors for Intelligent Track Skates at Railway Stations
Sensors 2017, 17(12), 2955; https://doi.org/10.3390/s17122955
Received: 24 November 2017 / Revised: 7 December 2017 / Accepted: 14 December 2017 / Published: 19 December 2017
Cited by 1 | PDF Full-text (3300 KB) | HTML Full-text | XML Full-text
Abstract
Anti-runaway prevention of rolling stocks at a railway station is essential in railway safety management. The traditional track skates for anti-runaway prevention of rolling stocks have some disadvantages since they are operated and monitored completely manually. This paper describes an anti-runaway prevention system
[...] Read more.
Anti-runaway prevention of rolling stocks at a railway station is essential in railway safety management. The traditional track skates for anti-runaway prevention of rolling stocks have some disadvantages since they are operated and monitored completely manually. This paper describes an anti-runaway prevention system (ARPS) based on intelligent track skates equipped with sensors and real-time monitoring and management system. This system, which has been updated from the traditional track skates, comprises four parts: intelligent track skates, a signal reader, a database station, and a monitoring system. This system can monitor the real-time situation of track skates without changing their workflow for anti-runaway prevention, and thus realize the integration of anti-runaway prevention information management. This system was successfully tested and practiced at Sunjia station in Harbin Railway Bureau in 2014, and the results confirmed that the system showed 100% accuracy in reflecting the usage status of the track skates. The system could meet practical demands, as it is highly reliable and supports long-distance communication. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
Sensors 2017, 17(12), 2854; https://doi.org/10.3390/s17122854
Received: 30 September 2017 / Revised: 28 November 2017 / Accepted: 2 December 2017 / Published: 8 December 2017
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Abstract
Vehicle sensor networks (VSNs) are ushering in a promising future by enabling more intelligent transportation systems and providing a more efficient driving experience. However, because of their inherent openness, VSNs are subject to a large number of potential security threats. Although various authentication
[...] Read more.
Vehicle sensor networks (VSNs) are ushering in a promising future by enabling more intelligent transportation systems and providing a more efficient driving experience. However, because of their inherent openness, VSNs are subject to a large number of potential security threats. Although various authentication schemes have been proposed for addressing security problems, they are not suitable for VSN applications because of their high computation and communication costs. Chuang and Lee have developed a trust-extended authentication mechanism (TEAM) for vehicle-to-vehicle communication using a transitive trust relationship, which they claim can resist various attacks. However, it fails to counter internal attacks because of the utilization of a shared secret key. In this paper, to eliminate the vulnerability of TEAM, an enhanced privacy-preserving authentication scheme for VSNs is constructed. The security of our proposed scheme is proven under the random oracle model based on the assumption of the computational Diffie–Hellman problem. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Sliding Mode Observer-Based Current Sensor Fault Reconstruction and Unknown Load Disturbance Estimation for PMSM Driven System
Sensors 2017, 17(12), 2833; https://doi.org/10.3390/s17122833
Received: 23 September 2017 / Revised: 2 December 2017 / Accepted: 3 December 2017 / Published: 6 December 2017
Cited by 4 | PDF Full-text (5967 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a new scheme of reconstructing current sensor faults and estimating unknown load disturbance for a permanent magnet synchronous motor (PMSM)-driven system. First, the original PMSM system is transformed into two subsystems; the first subsystem has unknown system load disturbances, which
[...] Read more.
This paper proposes a new scheme of reconstructing current sensor faults and estimating unknown load disturbance for a permanent magnet synchronous motor (PMSM)-driven system. First, the original PMSM system is transformed into two subsystems; the first subsystem has unknown system load disturbances, which are unrelated to sensor faults, and the second subsystem has sensor faults, but is free from unknown load disturbances. Introducing a new state variable, the augmented subsystem that has sensor faults can be transformed into having actuator faults. Second, two sliding mode observers (SMOs) are designed: the unknown load disturbance is estimated by the first SMO in the subsystem, which has unknown load disturbance, and the sensor faults can be reconstructed using the second SMO in the augmented subsystem, which has sensor faults. The gains of the proposed SMOs and their stability analysis are developed via the solution of linear matrix inequality (LMI). Finally, the effectiveness of the proposed scheme was verified by simulations and experiments. The results demonstrate that the proposed scheme can reconstruct current sensor faults and estimate unknown load disturbance for the PMSM-driven system. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle GNSS/Electronic Compass/Road Segment Information Fusion for Vehicle-to-Vehicle Collision Avoidance Application
Sensors 2017, 17(12), 2724; https://doi.org/10.3390/s17122724
Received: 24 September 2017 / Revised: 13 November 2017 / Accepted: 15 November 2017 / Published: 25 November 2017
Cited by 1 | PDF Full-text (6160 KB) | HTML Full-text | XML Full-text
Abstract
The increasing number of vehicles in modern cities brings the problem of increasing crashes. One of the applications or services of Intelligent Transportation Systems (ITS) conceived to improve safety and reduce congestion is collision avoidance. This safety critical application requires sub-meter level vehicle
[...] Read more.
The increasing number of vehicles in modern cities brings the problem of increasing crashes. One of the applications or services of Intelligent Transportation Systems (ITS) conceived to improve safety and reduce congestion is collision avoidance. This safety critical application requires sub-meter level vehicle state estimation accuracy with very high integrity, continuity and availability, to detect an impending collision and issue a warning or intervene in the case that the warning is not heeded. Because of the challenging city environment, to date there is no approved method capable of delivering this high level of performance in vehicle state estimation. In particular, the current Global Navigation Satellite System (GNSS) based collision avoidance systems have the major limitation that the real-time accuracy of dynamic state estimation deteriorates during abrupt acceleration and deceleration situations, compromising the integrity of collision avoidance. Therefore, to provide the Required Navigation Performance (RNP) for collision avoidance, this paper proposes a novel Particle Filter (PF) based model for the integration or fusion of real-time kinematic (RTK) GNSS position solutions with electronic compass and road segment data used in conjunction with an Autoregressive (AR) motion model. The real-time vehicle state estimates are used together with distance based collision avoidance algorithms to predict potential collisions. The algorithms are tested by simulation and in the field representing a low density urban environment. The results show that the proposed algorithm meets the horizontal positioning accuracy requirement for collision avoidance and is superior to positioning accuracy of GNSS only, traditional Constant Velocity (CV) and Constant Acceleration (CA) based motion models, with a significant improvement in the prediction accuracy of potential collision. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle An Orientation Sensor-Based Head Tracking System for Driver Behaviour Monitoring
Sensors 2017, 17(11), 2692; https://doi.org/10.3390/s17112692
Received: 16 October 2017 / Revised: 17 November 2017 / Accepted: 18 November 2017 / Published: 22 November 2017
Cited by 1 | PDF Full-text (6364 KB) | HTML Full-text | XML Full-text
Abstract
Although at present legislation does not allow drivers in a Level 3 autonomous vehicle to engage in a secondary task, there may become a time when it does. Monitoring the behaviour of drivers engaging in various non-driving activities (NDAs) is crucial to decide
[...] Read more.
Although at present legislation does not allow drivers in a Level 3 autonomous vehicle to engage in a secondary task, there may become a time when it does. Monitoring the behaviour of drivers engaging in various non-driving activities (NDAs) is crucial to decide how well the driver will be able to take over control of the vehicle. One limitation of the commonly used face-based head tracking system, using cameras, is that sufficient features of the face must be visible, which limits the detectable angle of head movement and thereby measurable NDAs, unless multiple cameras are used. This paper proposes a novel orientation sensor based head tracking system that includes twin devices, one of which measures the movement of the vehicle while the other measures the absolute movement of the head. Measurement error in the shaking and nodding axes were less than 0.4°, while error in the rolling axis was less than 2°. Comparison with a camera-based system, through in-house tests and on-road tests, showed that the main advantage of the proposed system is the ability to detect angles larger than 20° in the shaking and nodding axes. Finally, a case study demonstrated that the measurement of the shaking and nodding angles, produced from the proposed system, can effectively characterise the drivers’ behaviour while engaged in the NDAs of chatting to a passenger and playing on a smartphone. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle A LQR-Based Controller with Estimation of Road Bank for Improving Vehicle Lateral and Rollover Stability via Active Suspension
Sensors 2017, 17(10), 2318; https://doi.org/10.3390/s17102318
Received: 21 July 2017 / Revised: 9 October 2017 / Accepted: 10 October 2017 / Published: 13 October 2017
Cited by 2 | PDF Full-text (26035 KB) | HTML Full-text | XML Full-text
Abstract
In this article, a Linear Quadratic Regulator (LQR) lateral stability and rollover controller has been developed including as the main novelty taking into account the road bank angle and using exclusively active suspension for both lateral stability and rollover control. The main problem
[...] Read more.
In this article, a Linear Quadratic Regulator (LQR) lateral stability and rollover controller has been developed including as the main novelty taking into account the road bank angle and using exclusively active suspension for both lateral stability and rollover control. The main problem regarding the road bank is that it cannot be measured by means of on-board sensors. The solution proposed in this article is performing an estimation of this variable using a Kalman filter. In this way, it is possible to distinguish between the road disturbance component and the vehicle’s roll angle. The controller’s effectiveness has been tested by means of simulations carried out in TruckSim, using an experimentally-validated vehicle model. Lateral load transfer, roll angle, yaw rate and sideslip angle have been analyzed in order to quantify the improvements achieved on the behavior of the vehicle. For that purpose, these variables have been compared with the results obtained from both a vehicle that uses passive suspension and a vehicle using a fuzzy logic controller. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Evaluating Degradation at Railway Crossings Using Axle Box Acceleration Measurements
Sensors 2017, 17(10), 2236; https://doi.org/10.3390/s17102236
Received: 29 August 2017 / Revised: 22 September 2017 / Accepted: 26 September 2017 / Published: 29 September 2017
Cited by 5 | PDF Full-text (16009 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In this paper, we investigate the capability of an axle box acceleration (ABA) system to evaluate the degradation at railway crossings. For this purpose, information from multiple sensors, namely, ABA signals, 3D rail profiles, Global Positioning System (GPS) and tachometer recordings, was collected
[...] Read more.
In this paper, we investigate the capability of an axle box acceleration (ABA) system to evaluate the degradation at railway crossings. For this purpose, information from multiple sensors, namely, ABA signals, 3D rail profiles, Global Positioning System (GPS) and tachometer recordings, was collected from both nominal and degraded crossings. By proper correlation of the gathered data, an algorithm was proposed to distinguish the characteristic ABA related to the degradation and then to evaluate the health condition of crossings. The algorithm was then demonstrated on a crossing with an unknown degradation status, and its capability was verified via a 3D profile measurement. The results indicate that the ABA system is effective at monitoring two types of degradations. The first type is uneven deformation between the wing rail and crossing nose, corresponding to characteristic ABA frequencies of 230–350 and 460–650 Hz. The second type is local irregularity in the longitudinal slope of the crossing nose, corresponding to characteristic ABA frequencies of 460–650 Hz. The types and severity of the degradation can be evaluated by the spatial distribution and energy concentration of the characteristic frequencies of the ABA signals. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle DISPAQ: Distributed Profitable-Area Query from Big Taxi Trip Data
Sensors 2017, 17(10), 2201; https://doi.org/10.3390/s17102201
Received: 2 August 2017 / Revised: 8 September 2017 / Accepted: 19 September 2017 / Published: 25 September 2017
Cited by 1 | PDF Full-text (2875 KB) | HTML Full-text | XML Full-text
Abstract
One of the crucial problems for taxi drivers is to efficiently locate passengers in order to increase profits. The rapid advancement and ubiquitous penetration of Internet of Things (IoT) technology into transportation industries enables us to provide taxi drivers with locations that have
[...] Read more.
One of the crucial problems for taxi drivers is to efficiently locate passengers in order to increase profits. The rapid advancement and ubiquitous penetration of Internet of Things (IoT) technology into transportation industries enables us to provide taxi drivers with locations that have more potential passengers (more profitable areas) by analyzing and querying taxi trip data. In this paper, we propose a query processing system, called Distributed Profitable-Area Query (DISPAQ) which efficiently identifies profitable areas by exploiting the Apache Software Foundation’s Spark framework and a MongoDB database. DISPAQ first maintains a profitable-area query index (PQ-index) by extracting area summaries and route summaries from raw taxi trip data. It then identifies candidate profitable areas by searching the PQ-index during query processing. Then, it exploits a Z-Skyline algorithm, which is an extension of skyline processing with a Z-order space filling curve, to quickly refine the candidate profitable areas. To improve the performance of distributed query processing, we also propose local Z-Skyline optimization, which reduces the number of dominant tests by distributing killer profitable areas to each cluster node. Through extensive evaluation with real datasets, we demonstrate that our DISPAQ system provides a scalable and efficient solution for processing profitable-area queries from huge amounts of big taxi trip data. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Measurement of Non-Stationary Characteristics of a Landfall Typhoon at the Jiangyin Bridge Site
Sensors 2017, 17(10), 2186; https://doi.org/10.3390/s17102186
Received: 21 August 2017 / Revised: 12 September 2017 / Accepted: 20 September 2017 / Published: 22 September 2017
Cited by 1 | PDF Full-text (2970 KB) | HTML Full-text | XML Full-text
Abstract
The wind-sensitive long-span suspension bridge is a vital element in land transportation. Understanding the wind characteristics at the bridge site is thus of great significance to the wind- resistant analysis of such a flexible structure. In this study, a strong wind event from
[...] Read more.
The wind-sensitive long-span suspension bridge is a vital element in land transportation. Understanding the wind characteristics at the bridge site is thus of great significance to the wind- resistant analysis of such a flexible structure. In this study, a strong wind event from a landfall typhoon called Soudelor recorded at the Jiangyin Bridge site with the anemometer is taken as the research object. As inherent time-varying trends are frequently captured in typhoon events, the wind characteristics of Soudelor are analyzed in a non-stationary perspective. The time-varying mean is first extracted with the wavelet-based self-adaptive method. Then, the non-stationary turbulent wind characteristics, e.g.; turbulence intensity, gust factor, turbulence integral scale, and power spectral density, are investigated and compared with the results from the stationary analysis. The comparison highlights the importance of non-stationary considerations of typhoon events, and a transition from stationarity to non-stationarity for the analysis of wind effects. The analytical results could help enrich the database of non-stationary wind characteristics, and are expected to provide references for the wind-resistant analysis of engineering structures in similar areas. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle SFOL Pulse: A High Accuracy DME Pulse for Alternative Aircraft Position and Navigation
Sensors 2017, 17(10), 2183; https://doi.org/10.3390/s17102183
Received: 31 July 2017 / Revised: 17 September 2017 / Accepted: 20 September 2017 / Published: 22 September 2017
Cited by 7 | PDF Full-text (3166 KB) | HTML Full-text | XML Full-text
Abstract
In the Federal Aviation Administration’s (FAA) performance based navigation strategy announced in 2016, the FAA stated that it would retain and expand the Distance Measuring Equipment (DME) infrastructure to ensure resilient aircraft navigation capability during the event of a Global Navigation Satellite System
[...] Read more.
In the Federal Aviation Administration’s (FAA) performance based navigation strategy announced in 2016, the FAA stated that it would retain and expand the Distance Measuring Equipment (DME) infrastructure to ensure resilient aircraft navigation capability during the event of a Global Navigation Satellite System (GNSS) outage. However, the main drawback of the DME as a GNSS back up system is that it requires a significant expansion of the current DME ground infrastructure due to its poor distance measuring accuracy over 100 m. The paper introduces a method to improve DME distance measuring accuracy by using a new DME pulse shape. The proposed pulse shape was developed by using Genetic Algorithms and is less susceptible to multipath effects so that the ranging error reduces by 36.0–77.3% when compared to the Gaussian and Smoothed Concave Polygon DME pulses, depending on noise environment. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Railway Tunnel Clearance Inspection Method Based on 3D Point Cloud from Mobile Laser Scanning
Sensors 2017, 17(9), 2055; https://doi.org/10.3390/s17092055
Received: 12 July 2017 / Revised: 27 August 2017 / Accepted: 2 September 2017 / Published: 7 September 2017
Cited by 3 | PDF Full-text (7754 KB) | HTML Full-text | XML Full-text
Abstract
Railway tunnel clearance is directly related to the safe operation of trains and upgrading of freight capacity. As more and more railway are put into operation and the operation is continuously becoming faster, the railway tunnel clearance inspection should be more precise and
[...] Read more.
Railway tunnel clearance is directly related to the safe operation of trains and upgrading of freight capacity. As more and more railway are put into operation and the operation is continuously becoming faster, the railway tunnel clearance inspection should be more precise and efficient. In view of the problems existing in traditional tunnel clearance inspection methods, such as low density, slow speed and a lot of manual operations, this paper proposes a tunnel clearance inspection approach based on 3D point clouds obtained by a mobile laser scanning system (MLS). First, a dynamic coordinate system for railway tunnel clearance inspection has been proposed. A rail line extraction algorithm based on 3D linear fitting is implemented from the segmented point cloud to establish a dynamic clearance coordinate system. Second, a method to seamlessly connect all rail segments based on the railway clearance restrictions, and a seamless rail alignment is formed sequentially from the middle tunnel section to both ends. Finally, based on the rail alignment and the track clearance coordinate system, different types of clearance frames are introduced for intrusion operation with the tunnel section to realize the tunnel clearance inspection. By taking the Shuanghekou Tunnel of the Chengdu–Kunming Railway as an example, when the clearance inspection is carried out by the method mentioned herein, its precision can reach 0.03 m, and difference types of clearances can be effectively calculated. This method has a wide application prospects. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle An Adaptive Trajectory Clustering Method Based on Grid and Density in Mobile Pattern Analysis
Sensors 2017, 17(9), 2013; https://doi.org/10.3390/s17092013
Received: 14 July 2017 / Revised: 29 August 2017 / Accepted: 30 August 2017 / Published: 2 September 2017
Cited by 3 | PDF Full-text (2433 KB) | HTML Full-text | XML Full-text
Abstract
Clustering analysis is one of the most important issues in trajectory data mining. Trajectory clustering can be widely applied in the detection of hotspots, mobile pattern analysis, urban transportation control, and hurricane prediction, etc. To obtain good clustering performance, the existing trajectory clustering
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Clustering analysis is one of the most important issues in trajectory data mining. Trajectory clustering can be widely applied in the detection of hotspots, mobile pattern analysis, urban transportation control, and hurricane prediction, etc. To obtain good clustering performance, the existing trajectory clustering approaches need to input one or more parameters to calibrate the optimal values, which results in a heavy workload and computational complexity. To realize adaptive parameter calibration and reduce the workload of trajectory clustering, an adaptive trajectory clustering approach based on the grid and density (ATCGD) is proposed in this paper. The proposed ATCGD approach includes three parts: partition, mapping, and clustering. In the partition phase, ATCGD applies the average angular difference-based MDL (AD-MDL) partition method to ensure the partition accuracy on the premise that it decreases the number of the segments after the partition. During the mapping procedure, the partitioned segments are mapped into the corresponding cells, and the mapping relationship between the segments and the cells are stored. In the clustering phase, adopting the DBSCAN-based method, the segments in the cells are clustered on the basis of the calibrated values of parameters from the mapping procedure. The extensive experiments indicate that although the results of the adaptive parameter calibration are not optimal, in most cases, the difference between the adaptive calibration and the optimal is less than 5%, while the run time of clustering can reduce about 95%, compared with the TRACLUS algorithm. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability
Sensors 2017, 17(9), 1991; https://doi.org/10.3390/s17091991
Received: 23 April 2017 / Revised: 29 June 2017 / Accepted: 10 July 2017 / Published: 31 August 2017
Cited by 9 | PDF Full-text (1517 KB) | HTML Full-text | XML Full-text
Abstract
Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and
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Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor
Sensors 2017, 17(9), 1932; https://doi.org/10.3390/s17091932
Received: 27 June 2017 / Revised: 20 August 2017 / Accepted: 21 August 2017 / Published: 23 August 2017
Cited by 1 | PDF Full-text (13096 KB) | HTML Full-text | XML Full-text
Abstract
Object tracking is a crucial research subfield in computer vision and it has wide applications in navigation, robotics and military applications and so on. In this paper, the real-time visualization of 3D point clouds data based on the VLP-16 3D Light Detection and
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Object tracking is a crucial research subfield in computer vision and it has wide applications in navigation, robotics and military applications and so on. In this paper, the real-time visualization of 3D point clouds data based on the VLP-16 3D Light Detection and Ranging (LiDAR) sensor is achieved, and on the basis of preprocessing, fast ground segmentation, Euclidean clustering segmentation for outliers, View Feature Histogram (VFH) feature extraction, establishing object models and searching matching a moving spherical target, the Kalman filter and adaptive particle filter are used to estimate in real-time the position of a moving spherical target. The experimental results show that the Kalman filter has the advantages of high efficiency while adaptive particle filter has the advantages of high robustness and high precision when tested and validated on three kinds of scenes under the condition of target partial occlusion and interference, different moving speed and different trajectories. The research can be applied in the natural environment of fruit identification and tracking, robot navigation and control and other fields. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Moving Object Detection in Heterogeneous Conditions in Embedded Systems
Sensors 2017, 17(7), 1546; https://doi.org/10.3390/s17071546
Received: 25 May 2017 / Revised: 23 June 2017 / Accepted: 27 June 2017 / Published: 1 July 2017
Cited by 1 | PDF Full-text (2432 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper presents a system for moving object exposure, focusing on pedestrian detection, in external, unfriendly, and heterogeneous environments. The system manipulates and accurately merges information coming from subsequent video frames, making small computational efforts in each single frame. Its main characterizing feature
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This paper presents a system for moving object exposure, focusing on pedestrian detection, in external, unfriendly, and heterogeneous environments. The system manipulates and accurately merges information coming from subsequent video frames, making small computational efforts in each single frame. Its main characterizing feature is to combine several well-known movement detection and tracking techniques, and to orchestrate them in a smart way to obtain good results in diversified scenarios. It uses dynamically adjusted thresholds to characterize different regions of interest, and it also adopts techniques to efficiently track movements, and detect and correct false positives. Accuracy and reliability mainly depend on the overall receipt, i.e., on how the software system is designed and implemented, on how the different algorithmic phases communicate information and collaborate with each other, and on how concurrency is organized. The application is specifically designed to work with inexpensive hardware devices, such as off-the-shelf video cameras and small embedded computational units, eventually forming an intelligent urban grid. As a matter of fact, the major contribution of the paper is the presentation of a tool for real-time applications in embedded devices with finite computational (time and memory) resources. We run experimental results on several video sequences (both home-made and publicly available), showing the robustness and accuracy of the overall detection strategy. Comparisons with state-of-the-art strategies show that our application has similar tracking accuracy but much higher frame-per-second rates. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Physical Layer Secret-Key Generation Scheme for Transportation Security Sensor Network
Sensors 2017, 17(7), 1524; https://doi.org/10.3390/s17071524
Received: 13 April 2017 / Revised: 23 June 2017 / Accepted: 24 June 2017 / Published: 28 June 2017
Cited by 2 | PDF Full-text (869 KB) | HTML Full-text | XML Full-text
Abstract
Wireless Sensor Networks (WSNs) are widely used in different disciplines, including transportation systems, agriculture field environment monitoring, healthcare systems, and industrial monitoring. The security challenge of the wireless communication link between sensor nodes is critical in WSNs. In this paper, we propose a
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Wireless Sensor Networks (WSNs) are widely used in different disciplines, including transportation systems, agriculture field environment monitoring, healthcare systems, and industrial monitoring. The security challenge of the wireless communication link between sensor nodes is critical in WSNs. In this paper, we propose a new physical layer secret-key generation scheme for transportation security sensor network. The scheme is based on the cooperation of all the sensor nodes, thus avoiding the key distribution process, which increases the security of the system. Different passive and active attack models are analyzed in this paper. We also prove that when the cooperative node number is large enough, even when the eavesdropper is equipped with multiple antennas, the secret-key is still secure. Numerical results are performed to show the efficiency of the proposed scheme. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Towards the Internet of Smart Trains: A Review on Industrial IoT-Connected Railways
Sensors 2017, 17(6), 1457; https://doi.org/10.3390/s17061457
Received: 2 May 2017 / Revised: 18 June 2017 / Accepted: 19 June 2017 / Published: 21 June 2017
Cited by 18 | PDF Full-text (1566 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies
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Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies since the deployment of GSM-R, describing the main alternatives and how railway requirements, specifications and recommendations have evolved over time. The advantages of the latest generation of broadband communication systems (e.g., LTE, 5G, IEEE 802.11ad) and the emergence of Wireless Sensor Networks (WSNs) for the railway environment are also explained together with the strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on providing a holistic approach, identifying scenarios and architectures where railways could leverage better commercial IIoT capabilities. After reviewing the main industrial developments, short and medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall, it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art of different technologies and services that will revolutionize the railway industry and will allow for confronting today challenges. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle A New Filtering and Smoothing Algorithm for Railway Track Surveying Based on Landmark and IMU/Odometer
Sensors 2017, 17(6), 1438; https://doi.org/10.3390/s17061438
Received: 9 April 2017 / Revised: 16 June 2017 / Accepted: 16 June 2017 / Published: 19 June 2017
Cited by 3 | PDF Full-text (2553 KB) | HTML Full-text | XML Full-text
Abstract
High-accuracy railway track surveying is essential for railway construction and maintenance. The traditional approaches based on total station equipment are not efficient enough since high precision surveying frequently needs static measurements. This paper proposes a new filtering and smoothing algorithm based on the
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High-accuracy railway track surveying is essential for railway construction and maintenance. The traditional approaches based on total station equipment are not efficient enough since high precision surveying frequently needs static measurements. This paper proposes a new filtering and smoothing algorithm based on the IMU/odometer and landmarks integration for the railway track surveying. In order to overcome the difficulty of estimating too many error parameters with too few landmark observations, a new model with completely observable error states is established by combining error terms of the system. Based on covariance analysis, the analytical relationship between the railway track surveying accuracy requirements and equivalent gyro drifts including bias instability and random walk noise are established. Experiment results show that the accuracy of the new filtering and smoothing algorithm for railway track surveying can reach 1 mm (1σ) when using a Ring Laser Gyroscope (RLG)-based Inertial Measurement Unit (IMU) with gyro bias instability of 0.03°/h and random walk noise of 0.005 °h while control points of the track control network (CPIII) position observations are provided by the optical total station in about every 60 m interval. The proposed approach can satisfy at the same time the demands of high accuracy and work efficiency for railway track surveying. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Dynamic Speed Adaptation for Path Tracking Based on Curvature Information and Speed Limits
Sensors 2017, 17(6), 1383; https://doi.org/10.3390/s17061383
Received: 30 May 2017 / Revised: 8 June 2017 / Accepted: 8 June 2017 / Published: 14 June 2017
Cited by 7 | PDF Full-text (4452 KB) | HTML Full-text | XML Full-text
Abstract
A critical concern of autonomous vehicles is safety. Different approaches have tried to enhance driving safety to reduce the number of fatal crashes and severe injuries. As an example, Intelligent Speed Adaptation (ISA) systems warn the driver when the vehicle exceeds the recommended
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A critical concern of autonomous vehicles is safety. Different approaches have tried to enhance driving safety to reduce the number of fatal crashes and severe injuries. As an example, Intelligent Speed Adaptation (ISA) systems warn the driver when the vehicle exceeds the recommended speed limit. However, these systems only take into account fixed speed limits without considering factors like road geometry. In this paper, we consider road curvature with speed limits to automatically adjust vehicle’s speed with the ideal one through our proposed Dynamic Speed Adaptation (DSA) method. Furthermore, ‘curve analysis extraction’ and ‘speed limits database creation’ are also part of our contribution. An algorithm that analyzes GPS information off-line identifies high curvature segments and estimates the speed for each curve. The speed limit database contains information about the different speed limit zones for each traveled path. Our DSA senses speed limits and curves of the road using GPS information and ensures smooth speed transitions between current and ideal speeds. Through experimental simulations with different control algorithms on real and simulated datasets, we prove that our method is able to significantly reduce lateral errors on sharp curves, to respect speed limits and consequently increase safety and comfort for the passenger. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques
Sensors 2017, 17(6), 1350; https://doi.org/10.3390/s17061350
Received: 26 April 2017 / Revised: 1 June 2017 / Accepted: 7 June 2017 / Published: 10 June 2017
Cited by 7 | PDF Full-text (6996 KB) | HTML Full-text | XML Full-text
Abstract
Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To
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Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Structural Health Monitoring for a Z-Type Special Vehicle
Sensors 2017, 17(6), 1262; https://doi.org/10.3390/s17061262
Received: 2 April 2017 / Revised: 17 May 2017 / Accepted: 22 May 2017 / Published: 1 June 2017
Cited by 1 | PDF Full-text (4340 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays there exist various kinds of special vehicles designed for some purposes, which are different from regular vehicles in overall dimension and design. In that case, accidents such as overturning will lead to large economical loss and casualties. There are still no technical
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Nowadays there exist various kinds of special vehicles designed for some purposes, which are different from regular vehicles in overall dimension and design. In that case, accidents such as overturning will lead to large economical loss and casualties. There are still no technical specifications to follow to ensure the safe operation and driving of these special vehicles. Owing to the poor efficiency of regular maintenance, it is more feasible and effective to apply real-time monitoring during the operation and driving process. In this paper, the fiber Bragg grating (FBG) sensors are used to monitor the safety of a z-type special vehicle. Based on the structural features and force distribution, a reasonable structural health monitoring (SHM) scheme is presented. Comparing the monitoring results with the finite element simulation results guarantees the accuracy and reliability of the monitoring results. Large amounts of data are collected during the operation and driving progress to evaluate the structural safety condition and provide reference for SHM systems developed for other special vehicles. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Automatic Detection of Driver Fatigue Using Driving Operation Information for Transportation Safety
Sensors 2017, 17(6), 1212; https://doi.org/10.3390/s17061212
Received: 19 April 2017 / Revised: 19 May 2017 / Accepted: 24 May 2017 / Published: 25 May 2017
Cited by 4 | PDF Full-text (1553 KB) | HTML Full-text | XML Full-text
Abstract
Fatigued driving is a major cause of road accidents. For this reason, the method in this paper is based on the steering wheel angles (SWA) and yaw angles (YA) information under real driving conditions to detect drivers’ fatigue levels. It analyzes the operation
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Fatigued driving is a major cause of road accidents. For this reason, the method in this paper is based on the steering wheel angles (SWA) and yaw angles (YA) information under real driving conditions to detect drivers’ fatigue levels. It analyzes the operation features of SWA and YA under different fatigue statuses, then calculates the approximate entropy (ApEn) features of a short sliding window on time series. Using the nonlinear feature construction theory of dynamic time series, with the fatigue features as input, designs a “2-6-6-3” multi-level back propagation (BP) Neural Networks classifier to realize the fatigue detection. An approximately 15-h experiment is carried out on a real road, and the data retrieved are segmented and labeled with three fatigue levels after expert evaluation, namely “awake”, “drowsy” and “very drowsy”. The average accuracy of 88.02% in fatigue identification was achieved in the experiment, endorsing the value of the proposed method for engineering applications. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Traffic Sign Detection System for Locating Road Intersections and Roundabouts: The Chilean Case
Sensors 2017, 17(6), 1207; https://doi.org/10.3390/s17061207
Received: 2 January 2017 / Revised: 16 May 2017 / Accepted: 22 May 2017 / Published: 25 May 2017
Cited by 5 | PDF Full-text (5106 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a traffic sign detection method for signs close to road intersections and roundabouts, such as stop and yield (give way) signs. The proposed method relies on statistical templates built using color information for both segmentation and classification. The segmentation method
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This paper presents a traffic sign detection method for signs close to road intersections and roundabouts, such as stop and yield (give way) signs. The proposed method relies on statistical templates built using color information for both segmentation and classification. The segmentation method uses the RGB-normalized (ErEgEb) color space for ROIs (Regions of Interest) generation based on a chromaticity filter, where templates at 10 scales are applied to the entire image. Templates consider the mean and standard deviation of normalized color of the traffic signs to build thresholding intervals where the expected color should lie for a given sign. The classification stage employs the information of the statistical templates over YCbCr and ErEgEb color spaces, for which the background has been previously removed by using a probability function that models the probability that the pixel corresponds to a sign given its chromaticity values. This work includes an analysis of the detection rate as a function of the distance between the vehicle and the sign. Such information is useful to validate the robustness of the approach and is often not included in the existing literature. The detection rates, as a function of distance, are compared to those of the well-known Viola–Jones method. The results show that for distances less than 48 m, the proposed method achieves a detection rate of 87.5 % and 95.4 % for yield and stop signs, respectively. For distances less than 30 m, the detection rate is 100 % for both signs. The Viola–Jones approach has detection rates below 20 % for distances between 30 and 48 m, and barely improves in the 20–30 m range with detection rates of up to 60 % . Thus, the proposed method provides a robust alternative for intersection detection that relies on statistical color-based templates instead of shape information. The experiments employed videos of traffic signs taken in several streets of Santiago, Chile, using a research platform implemented at the Robotics and Automation Laboratory of PUC to develop driver assistance systems. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Multiple Two-Way Time Message Exchange (TTME) Time Synchronization for Bridge Monitoring Wireless Sensor Networks
Sensors 2017, 17(5), 1027; https://doi.org/10.3390/s17051027
Received: 5 March 2017 / Revised: 19 April 2017 / Accepted: 27 April 2017 / Published: 4 May 2017
Cited by 6 | PDF Full-text (3079 KB) | HTML Full-text | XML Full-text
Abstract
Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important
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Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States
Sensors 2017, 17(5), 987; https://doi.org/10.3390/s17050987
Received: 27 February 2017 / Revised: 20 April 2017 / Accepted: 24 April 2017 / Published: 29 April 2017
Cited by 1 | PDF Full-text (15542 KB) | HTML Full-text | XML Full-text
Abstract
Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33% of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC)
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Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33 % of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle’s parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle’s roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle’s states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Shadow-Based Vehicle Detection in Urban Traffic
Sensors 2017, 17(5), 975; https://doi.org/10.3390/s17050975
Received: 17 March 2017 / Revised: 19 April 2017 / Accepted: 22 April 2017 / Published: 27 April 2017
Cited by 3 | PDF Full-text (3451 KB) | HTML Full-text | XML Full-text
Abstract
Vehicle detection is a fundamental task in Forward Collision Avoiding Systems (FACS). Generally, vision-based vehicle detection methods consist of two stages: hypotheses generation and hypotheses verification. In this paper, we focus on the former, presenting a feature-based method for on-road vehicle detection in
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Vehicle detection is a fundamental task in Forward Collision Avoiding Systems (FACS). Generally, vision-based vehicle detection methods consist of two stages: hypotheses generation and hypotheses verification. In this paper, we focus on the former, presenting a feature-based method for on-road vehicle detection in urban traffic. Hypotheses for vehicle candidates are generated according to the shadow under the vehicles by comparing pixel properties across the vertical intensity gradients caused by shadows on the road, and followed by intensity thresholding and morphological discrimination. Unlike methods that identify the shadow under a vehicle as a road region with intensity smaller than a coarse lower bound of the intensity for road, the thresholding strategy we propose determines a coarse upper bound of the intensity for shadow which reduces false positives rates. The experimental results are promising in terms of detection performance and robustness in day time under different weather conditions and cluttered scenarios to enable validation for the first stage of a complete FACS. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Multiple Objects Fusion Tracker Using a Matching Network for Adaptively Represented Instance Pairs
Sensors 2017, 17(4), 883; https://doi.org/10.3390/s17040883
Received: 27 February 2017 / Revised: 14 April 2017 / Accepted: 14 April 2017 / Published: 18 April 2017
PDF Full-text (1608 KB) | HTML Full-text | XML Full-text
Abstract
Multiple-object tracking is affected by various sources of distortion, such as occlusion, illumination variations and motion changes. Overcoming these distortions by tracking on RGB frames, such as shifting, has limitations because of material distortions caused by RGB frames. To overcome these distortions, we
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Multiple-object tracking is affected by various sources of distortion, such as occlusion, illumination variations and motion changes. Overcoming these distortions by tracking on RGB frames, such as shifting, has limitations because of material distortions caused by RGB frames. To overcome these distortions, we propose a multiple-object fusion tracker (MOFT), which uses a combination of 3D point clouds and corresponding RGB frames. The MOFT uses a matching function initialized on large-scale external sequences to determine which candidates in the current frame match with the target object in the previous frame. After conducting tracking on a few frames, the initialized matching function is fine-tuned according to the appearance models of target objects. The fine-tuning process of the matching function is constructed as a structured form with diverse matching function branches. In general multiple object tracking situations, scale variations for a scene occur depending on the distance between the target objects and the sensors. If the target objects in various scales are equally represented with the same strategy, information losses will occur for any representation of the target objects. In this paper, the output map of the convolutional layer obtained from a pre-trained convolutional neural network is used to adaptively represent instances without information loss. In addition, MOFT fuses the tracking results obtained from each modality at the decision level to compensate the tracking failures of each modality using basic belief assignment, rather than fusing modalities by selectively using the features of each modality. Experimental results indicate that the proposed tracker provides state-of-the-art performance considering multiple objects tracking (MOT) and KITTIbenchmarks. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network
Sensors 2017, 17(4), 853; https://doi.org/10.3390/s17040853
Received: 19 December 2016 / Revised: 15 March 2017 / Accepted: 16 March 2017 / Published: 13 April 2017
Cited by 14 | PDF Full-text (27589 KB) | HTML Full-text | XML Full-text
Abstract
Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘pedestrian crossing’
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Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘pedestrian crossing’ indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
Sensors 2017, 17(4), 818; https://doi.org/10.3390/s17040818
Received: 30 January 2017 / Revised: 18 March 2017 / Accepted: 7 April 2017 / Published: 10 April 2017
Cited by 50 | PDF Full-text (2637 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional
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This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata
Sensors 2017, 17(4), 716; https://doi.org/10.3390/s17040716
Received: 24 January 2017 / Revised: 20 March 2017 / Accepted: 23 March 2017 / Published: 29 March 2017
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Abstract
In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow
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In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems
Sensors 2017, 17(3), 524; https://doi.org/10.3390/s17030524
Received: 7 January 2017 / Revised: 1 March 2017 / Accepted: 1 March 2017 / Published: 6 March 2017
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Abstract
With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns
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With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions
Sensors 2017, 17(3), 495; https://doi.org/10.3390/s17030495
Received: 1 January 2017 / Revised: 8 February 2017 / Accepted: 28 February 2017 / Published: 2 March 2017
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Abstract
This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn)featuresfromfixedslidingwindowsonreal-timesteeringwheelanglestimeseries. Afterthat,
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This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn)featuresfromfixedslidingwindowsonreal-timesteeringwheelanglestimeseries. Afterthat, this system linearizes the ApEn features series through an adaptive piecewise linear fitting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classifier. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: “wake” and “drowsy”. The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the “awake” state, and 15.15% false detections of the “drowsy” state. The results also confirm that the proposed method based on SWA signal is valuable for applications in preventing traffic accidents caused by driver fatigue. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG
Sensors 2017, 17(3), 486; https://doi.org/10.3390/s17030486
Received: 25 December 2016 / Revised: 23 February 2017 / Accepted: 27 February 2017 / Published: 1 March 2017
Cited by 15 | PDF Full-text (8648 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using
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The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle A Framework for Bus Trajectory Extraction and Missing Data Recovery for Data Sampled from the Internet
Sensors 2017, 17(2), 342; https://doi.org/10.3390/s17020342
Received: 25 November 2016 / Revised: 18 January 2017 / Accepted: 4 February 2017 / Published: 10 February 2017
Cited by 2 | PDF Full-text (1321 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper presents a novel framework for trajectories’ extraction and missing data recovery for bus traveling data sampled from the Internet. The trajectory extraction procedure is composed of three main parts: trajectory clustering, trajectory cleaning and trajectory connecting. In the clustering procedure, we
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This paper presents a novel framework for trajectories’ extraction and missing data recovery for bus traveling data sampled from the Internet. The trajectory extraction procedure is composed of three main parts: trajectory clustering, trajectory cleaning and trajectory connecting. In the clustering procedure, we focus on feature construction and parameter selection for the fuzzy C-means clustering method. Following the clustering procedure, the trajectory cleaning algorithm is implemented based on a new introduced fuzzy connecting matrix, which evaluates the possibility of data belonging to the same trajectory and helps detect the anomalies in a ranked context-related order. Finally, the trajectory connecting algorithm is proposed to solve the issue that occurs in some cases when a route trajectory is incorrectly partitioned into several clusters. In the missing data recovery procedure, we developed the contextual linear interpolation for the cases of missing data occurring inside the trajectory and the median value interpolation for the cases of missing data outside the trajectory. Extensive experiments are conducted to demonstrate that the proposed framework offers a powerful ability to extract and recovery bus trajectories sampled from the Internet. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining
Sensors 2017, 17(2), 336; https://doi.org/10.3390/s17020336
Received: 14 December 2016 / Revised: 6 February 2017 / Accepted: 6 February 2017 / Published: 10 February 2017
Cited by 22 | PDF Full-text (30073 KB) | HTML Full-text | XML Full-text
Abstract
Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the
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Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Wind Tunnel Measurements for Flutter of a Long-Afterbody Bridge Deck
Sensors 2017, 17(2), 335; https://doi.org/10.3390/s17020335
Received: 28 November 2016 / Revised: 4 February 2017 / Accepted: 6 February 2017 / Published: 9 February 2017
Cited by 6 | PDF Full-text (3413 KB) | HTML Full-text | XML Full-text
Abstract
Bridges are an important component of transportation. Flutter is a self-excited, large amplitude vibration, which may lead to collapse of bridges. It must be understood and avoided. This paper takes the Jianghai Channel Bridge, which is a significant part of the Hong Kong-Zhuhai-Macao
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Bridges are an important component of transportation. Flutter is a self-excited, large amplitude vibration, which may lead to collapse of bridges. It must be understood and avoided. This paper takes the Jianghai Channel Bridge, which is a significant part of the Hong Kong-Zhuhai-Macao Bridge, as an example to investigate the flutter of the bridge deck. Firstly, aerodynamic force models for flutter of bridges were introduced. Then, wind tunnel tests of the bridge deck during the construction and the operation stages, under different wind attack angles and wind velocities, were carried out using a high frequency base balance (HFBB) system and laser displacement sensors. From the tests, the static aerodynamic forces and flutter derivatives of the bridge deck were observed. Correspondingly, the critical flutter wind speeds of the bridge deck were determined based on the derivatives, and they are compared with the directly measured flutter speeds. Results show that the observed derivatives are reasonable and applicable. Furthermore, the critical wind speeds in the operation stage is smaller than those in the construction stage. Besides, the flutter instabilities of the bridge in the construction and the operation stages are good. This study helps guarantee the design and the construction of the Jianghai Channel Bridge, and advances the understanding of flutter of long afterbody bridge decks. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle A Study on the Influence of Speed on Road Roughness Sensing: The SmartRoadSense Case
Sensors 2017, 17(2), 305; https://doi.org/10.3390/s17020305
Received: 27 December 2016 / Revised: 31 January 2017 / Accepted: 2 February 2017 / Published: 7 February 2017
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Abstract
SmartRoadSense is a crowdsensing project aimed at monitoring the conditions of the road surface. Using the sensors of a smartphone, SmartRoadSense monitors the vertical accelerations inside a vehicle traveling the road and extracts a roughness index conveying information about the road conditions. The
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SmartRoadSense is a crowdsensing project aimed at monitoring the conditions of the road surface. Using the sensors of a smartphone, SmartRoadSense monitors the vertical accelerations inside a vehicle traveling the road and extracts a roughness index conveying information about the road conditions. The roughness index and the smartphone GPS data are periodically sent to a central server where they are processed, associated with the specific road, and aggregated with data measured by other smartphones. This paper studies how the smartphone vertical accelerations and the roughness index are related to the vehicle speed. It is shown that the dependence can be locally approximated with a gamma (power) law. Extensive experimental results using data extracted from SmartRoadSense database confirm the gamma law relationship between the roughness index and the vehicle speed. The gamma law is then used for improving the SmartRoadSense data aggregation accounting for the effect of vehicle speed. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Camber Angle Inspection for Vehicle Wheel Alignments
Sensors 2017, 17(2), 285; https://doi.org/10.3390/s17020285
Received: 10 November 2016 / Revised: 5 January 2017 / Accepted: 24 January 2017 / Published: 3 February 2017
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Abstract
This paper introduces an alternative approach to the camber angle measurement for vehicle wheel alignment. Instead of current commercial approaches that apply computation vision techniques, this study aims at realizing a micro-control-unit (MCU)-based camber inspection system with a 3-axis accelerometer. We analyze the
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This paper introduces an alternative approach to the camber angle measurement for vehicle wheel alignment. Instead of current commercial approaches that apply computation vision techniques, this study aims at realizing a micro-control-unit (MCU)-based camber inspection system with a 3-axis accelerometer. We analyze the precision of the inspection system for the axis misalignments of the accelerometer. The results show that the axes of the accelerometer can be aligned to the axes of the camber inspection system imperfectly. The calibrations that can amend these axis misalignments between the camber inspection system and the accelerometer are also originally proposed since misalignments will usually happen in fabrications of the inspection systems. During camber angle measurements, the x-axis or z-axis of the camber inspection system and the wheel need not be perfectly aligned in the proposed approach. We accomplished two typical authentic camber angle measurements. The results show that the proposed approach is applicable with a precision of ± 0.015 and therefore facilitates the camber measurement process without downgrading the precision by employing an appropriate 3-axis accelerometer. In addition, the measured results of camber angles can be transmitted via the medium such as RS232, Bluetooth, and Wi-Fi. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor
Sensors 2017, 17(2), 263; https://doi.org/10.3390/s17020263
Received: 16 November 2016 / Revised: 18 January 2017 / Accepted: 23 January 2017 / Published: 29 January 2017
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Abstract
Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect—using electric current shape analysis—for the purpose of replacement condition detection of railway
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Detecting replacement conditions of railway point machines is important to simultaneously satisfy the budget-limit and train-safety requirements. In this study, we consider classification of the subtle differences in the aging effect—using electric current shape analysis—for the purpose of replacement condition detection of railway point machines. After analyzing the shapes of after-replacement data and then labeling the shapes of each before-replacement data, we can derive the criteria that can handle the subtle differences between “does-not-need-to-be-replaced” and “needs-to-be-replaced” shapes. On the basis of the experimental results with in-field replacement data, we confirmed that the proposed method could detect the replacement conditions with acceptable accuracy, as well as provide visual interpretability of the criteria used for the time-series classification. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle An Information Retrieval Approach for Robust Prediction of Road Surface States
Sensors 2017, 17(2), 262; https://doi.org/10.3390/s17020262
Received: 29 November 2016 / Revised: 19 January 2017 / Accepted: 23 January 2017 / Published: 28 January 2017
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Abstract
Recently, due to the increasing importance of reducing severe vehicle accidents on roads (especially on highways), the automatic identification of road surface conditions, and the provisioning of such information to drivers in advance, have recently been gaining significant momentum as a proactive solution
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Recently, due to the increasing importance of reducing severe vehicle accidents on roads (especially on highways), the automatic identification of road surface conditions, and the provisioning of such information to drivers in advance, have recently been gaining significant momentum as a proactive solution to decrease the number of vehicle accidents. In this paper, we firstly propose an information retrieval approach that aims to identify road surface states by combining conventional machine-learning techniques and moving average methods. Specifically, when signal information is received from a radar system, our approach attempts to estimate the current state of the road surface based on the similar instances observed previously based on utilizing a given similarity function. Next, the estimated state is then calibrated by using the recently estimated states to yield both effective and robust prediction results. To validate the performances of the proposed approach, we established a real-world experimental setting on a section of actual highway in South Korea and conducted a comparison with the conventional approaches in terms of accuracy. The experimental results show that the proposed approach successfully outperforms the previously developed methods. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Trust-Based Cooperative Social System Applied to a Carpooling Platform for Smartphones
Sensors 2017, 17(2), 245; https://doi.org/10.3390/s17020245
Received: 9 December 2016 / Accepted: 24 January 2017 / Published: 27 January 2017
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Abstract
One of the worst traffic problems today is the existence of huge traffic jams in almost any big city, produced by the large number of commuters using private cars. This problem has led to an increase in research on the optimization of vehicle
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One of the worst traffic problems today is the existence of huge traffic jams in almost any big city, produced by the large number of commuters using private cars. This problem has led to an increase in research on the optimization of vehicle occupancy in urban areas as this would help to solve the problem that most cars are occupied by single passengers. The solution of sharing the available seats in cars, known as carpooling, is already available in major cities around the world. However, carpooling is still not considered a safe and reliable solution for many users. With the widespread use of mobile technology and social networks, it is possible to create a trust-based platform to promote carpooling through a convenient, fast and secure system. The main objective of this work is the design and implementation of a carpool system that improves some important aspects of previous systems, focusing on trust between users, and on the security of the system. The proposed system guarantees user privacy and measures trust levels through a new reputation algorithm. In addition to this, the proposal has been developed as a mobile application for devices using the Android Open Source Project. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems
Sensors 2017, 17(1), 207; https://doi.org/10.3390/s17010207
Received: 13 October 2016 / Revised: 6 January 2017 / Accepted: 16 January 2017 / Published: 22 January 2017
Cited by 7 | PDF Full-text (917 KB) | HTML Full-text | XML Full-text
Abstract
To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from
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To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle A Cooperative Traffic Control of Vehicle–Intersection (CTCVI) for the Reduction of Traffic Delays and Fuel Consumption
Sensors 2016, 16(12), 2175; https://doi.org/10.3390/s16122175
Received: 18 October 2016 / Revised: 12 December 2016 / Accepted: 15 December 2016 / Published: 17 December 2016
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Abstract
The problem of reducing traffic delays and decreasing fuel consumption simultaneously in a network of intersections without traffic lights is solved by a cooperative traffic control algorithm, where the cooperation is executed based on the connection of Vehicle-to-Infrastructure (V2I). This resolution of the
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The problem of reducing traffic delays and decreasing fuel consumption simultaneously in a network of intersections without traffic lights is solved by a cooperative traffic control algorithm, where the cooperation is executed based on the connection of Vehicle-to-Infrastructure (V2I). This resolution of the problem contains two main steps. The first step concerns the itinerary of which intersections are chosen by vehicles to arrive at their destination from their starting point. Based on the principle of minimal travel distance, each vehicle chooses its itinerary dynamically based on the traffic loads in the adjacent intersections. The second step is related to the following proposed cooperative procedures to allow vehicles to pass through each intersection rapidly and economically: on one hand, according to the real-time information sent by vehicles via V2I in the edge of the communication zone, each intersection applies Dynamic Programming (DP) to cooperatively optimize the vehicle passing sequence with minimal traffic delays so that the vehicles may rapidly pass the intersection under the relevant safety constraints; on the other hand, after receiving this sequence, each vehicle finds the optimal speed profiles with the minimal fuel consumption by an exhaustive search. The simulation results reveal that the proposed algorithm can significantly reduce both travel delays and fuel consumption compared with other papers under different traffic volumes. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessArticle Thermal Property Analysis of Axle Load Sensors for Weighing Vehicles in Weigh-in-Motion System
Sensors 2016, 16(12), 2143; https://doi.org/10.3390/s16122143
Received: 6 October 2016 / Revised: 25 November 2016 / Accepted: 29 November 2016 / Published: 15 December 2016
Cited by 3 | PDF Full-text (3685 KB) | HTML Full-text | XML Full-text
Abstract
Systems which permit the weighing of vehicles in motion are called dynamic Weigh-in-Motion scales. In such systems, axle load sensors are embedded in the pavement. Among the influencing factors that negatively affect weighing accuracy is the pavement temperature. This paper presents a detailed
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Systems which permit the weighing of vehicles in motion are called dynamic Weigh-in-Motion scales. In such systems, axle load sensors are embedded in the pavement. Among the influencing factors that negatively affect weighing accuracy is the pavement temperature. This paper presents a detailed analysis of this phenomenon and describes the properties of polymer, quartz and bending plate load sensors. The studies were conducted in two ways: at roadside Weigh-in-Motion sites and at a laboratory using a climate chamber. For accuracy assessment of roadside systems, the reference vehicle method was used. The pavement temperature influence on the weighing error was experimentally investigated as well as a non-uniform temperature distribution along and across the Weigh-in-Motion site. Tests carried out in the climatic chamber allowed the influence of temperature on the sensor intrinsic error to be determined. The results presented clearly show that all kinds of sensors are temperature sensitive. This is a new finding, as up to now the quartz and bending plate sensors were considered insensitive to this factor. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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Open AccessReview Deployment of a Smart Structural Health Monitoring System for Long-Span Arch Bridges: A Review and a Case Study
Sensors 2017, 17(9), 2151; https://doi.org/10.3390/s17092151
Received: 25 July 2017 / Revised: 9 September 2017 / Accepted: 11 September 2017 / Published: 19 September 2017
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Abstract
Structural health monitoring (SHM) technology for surveillance and evaluation of existing and newly built long-span bridges has been widely developed, and the significance of the technique has been recognized by many administrative authorities. The paper reviews the recent progress of the SHM technology
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Structural health monitoring (SHM) technology for surveillance and evaluation of existing and newly built long-span bridges has been widely developed, and the significance of the technique has been recognized by many administrative authorities. The paper reviews the recent progress of the SHM technology that has been applied to long-span bridges. The deployment of a SHM system is introduced. Subsequently, the data analysis and condition assessment including techniques on modal identification, methods on signal processing, and damage identification were reviewed and summarized. A case study about a SHM system of a long-span arch bridge (the Jiubao bridge in China) was systematically incorporated in each part to advance our understanding of deployment and investigation of a SHM system for long-span arch bridges. The applications of SHM systems of long-span arch bridge were also introduced. From the illustrations, the challenges and future trends for development a SHM system were concluded. Full article
(This article belongs to the Special Issue Sensors for Transportation)
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