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Special Issue "Advance in Sensors and Sensing Systems for Driving and Transport"

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

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editor

Guest Editor
Prof. Dr. Radu Danescu

Department of Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Website | E-Mail
Interests: computer vision, stereovision, tracking, probabilistic estimation, machine learning

Special Issue Information

Dear Colleagues,

Today, transportation and driving face multiple difficult challenges. The cities and the highways become increasingly crowded, traffic accidents claim many lives, the energy resources are limited, pollution causes a wide range of problems such as global warming and damage to wildlife and to the human health, and the population in the developed world is aging rapidly, a process that limits the driving capacity and therefore the mobility.

Faced with these challenges, the transportation industries turn to automating some or all the tasks of driving, aiming to increase traffic safety, reduce congestion, reduce energy consumption and pollution, and help the impaired or elderly people keep their mobility.

A crucial aspect of automating the driving tasks is reliable sensing of the environment: position of other traffic participants, their speed, their type, the state of the vehicle itself, the situation of the traffic beyond the vehicle sensing area, weather conditions, road surface condition, and many more.

This Special Issue aims to highlight recent advances in sensors and sensing systems for driving and transport. Topics include, but are not limited, to:

  • Laser and radar sensor technologies and processing
  • Video and image sensing technologies and processing
  • Vehicle to infrastructure and vehicle to vehicle communication
  • Driver condition sensing and monitoring
  • Vehicle condition sensing and monitoring
  • Human machine interaction sensing
  • Weather condition sensing
  • Sensor models for environment perception
  • Automatic sensor calibration

 

Prof. Dr. Radu Danescu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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

  • Imaging sensors
  • Range sensors
  • Inertial sensors
  • Environment sensing
  • In-vehicle sensors
  • Sensor models
  • Sensor data processing
  • Autonomous vehicles

Published Papers (12 papers)

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Research

Open AccessArticle
Prototyping a System for Truck Differential Lock Control
Sensors 2019, 19(16), 3619; https://doi.org/10.3390/s19163619
Received: 17 July 2019 / Revised: 13 August 2019 / Accepted: 16 August 2019 / Published: 20 August 2019
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Abstract
The article deals with the development of a mechatronic system for locking vehicle differentials. An important benefit of this system is that it prevents the jamming of the vehicle in difficult adhesion conditions. The system recognizes such a situation much sooner than the [...] Read more.
The article deals with the development of a mechatronic system for locking vehicle differentials. An important benefit of this system is that it prevents the jamming of the vehicle in difficult adhesion conditions. The system recognizes such a situation much sooner than the driver and is able to respond immediately, ensuring smooth driving in off-road or snowy conditions. This article describes the control algorithm of this mechatronic system, which is designed for firefighting, military, or civilian vehicles with a drivetrain configuration of up to 10 × 10, and also explains the input signal processing and the control of actuators. The main part of this article concerns prototype testing on a vehicle. The results are an evaluation of one of the many experiments and monitor the proper function of the developed mechatronic system. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
Open AccessArticle
Modeling and Control of a Six Degrees of Freedom Maglev Vibration Isolation System
Sensors 2019, 19(16), 3608; https://doi.org/10.3390/s19163608
Received: 6 July 2019 / Revised: 13 August 2019 / Accepted: 16 August 2019 / Published: 19 August 2019
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Abstract
The environment in space provides favorable conditions for space missions. However, low frequency vibration poses a great challenge to high sensitivity equipment, resulting in performance degradation of sensitive systems. Due to the ever-increasing requirements to protect sensitive payloads, there is a pressing need [...] Read more.
The environment in space provides favorable conditions for space missions. However, low frequency vibration poses a great challenge to high sensitivity equipment, resulting in performance degradation of sensitive systems. Due to the ever-increasing requirements to protect sensitive payloads, there is a pressing need for micro-vibration suppression. This paper deals with the modeling and control of a maglev vibration isolation system. A high-precision nonlinear dynamic model with six degrees of freedom was derived, which contains the mathematical model of Lorentz actuators and umbilical cables. Regarding the system performance, a double closed-loop control strategy was proposed, and a sliding mode control algorithm was adopted to improve the vibration isolation performance. A simulation program of the system was developed in a MATLAB environment. A vibration isolation performance in the frequency range of 0.01–100 Hz and a tracking performance below 0.01 Hz were obtained. In order to verify the nonlinear dynamic model and the isolation performance, a principle prototype of the maglev isolation system equipped with accelerometers and position sensors was developed for the experiments. By comparing the simulation results and the experiment results, the nonlinear dynamic model of the maglev vibration isolation system was verified and the control strategy of the system was proved to be highly effective. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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Open AccessArticle
Sensorless Control of the Permanent Magnet Synchronous Motor
Sensors 2019, 19(16), 3546; https://doi.org/10.3390/s19163546
Received: 1 July 2019 / Revised: 4 August 2019 / Accepted: 8 August 2019 / Published: 14 August 2019
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Abstract
This paper describes the study and experimental verification of sensorless control of permanent magnet synchronous motors with a high precision drive using two novel estimation methods. All the studies of the modified Luenberger observer, reference model, and unscented Kalman filter are presented with [...] Read more.
This paper describes the study and experimental verification of sensorless control of permanent magnet synchronous motors with a high precision drive using two novel estimation methods. All the studies of the modified Luenberger observer, reference model, and unscented Kalman filter are presented with algorithm details. The main part determines trials with a full range of reference speeds with a special near-zero speed area taken into account. In order to compare the estimation performances of the observers, both are designed for the same motor and control system and run in the same environment. The experimental results indicate that the presented methods are capable of tracking the actual values of speed and motor position with small deviation, sufficient for precise control. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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Open AccessArticle
Autonomous Vision-Based Aerial Grasping for Rotorcraft Unmanned Aerial Vehicles
Sensors 2019, 19(15), 3410; https://doi.org/10.3390/s19153410
Received: 30 June 2019 / Revised: 29 July 2019 / Accepted: 1 August 2019 / Published: 3 August 2019
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Abstract
Autonomous vision-based aerial grasping is an essential and challenging task for aerial manipulation missions. In this paper, we propose a vision-based aerial grasping system for a Rotorcraft Unmanned Aerial Vehicle (UAV) to grasp a target object. The UAV system is equipped with a [...] Read more.
Autonomous vision-based aerial grasping is an essential and challenging task for aerial manipulation missions. In this paper, we propose a vision-based aerial grasping system for a Rotorcraft Unmanned Aerial Vehicle (UAV) to grasp a target object. The UAV system is equipped with a monocular camera, a 3-DOF robotic arm with a gripper and a Jetson TK1 computer. Efficient and reliable visual detectors and control laws are crucial for autonomous aerial grasping using limited onboard sensing and computational capabilities. To detect and track the target object in real time, an efficient proposal algorithm is presented to reliably estimate the region of interest (ROI), then a correlation filter-based classifier is developed to track the detected object. Moreover, a support vector regression (SVR)-based grasping position detector is proposed to improve the grasp success rate with high computational efficiency. Using the estimated grasping position and the UAV?Äôs states, novel control laws of the UAV and the robotic arm are proposed to perform aerial grasping. Extensive simulations and outdoor flight experiments have been implemented. The experimental results illustrate that the proposed vision-based aerial grasping system can autonomously and reliably grasp the target object while working entirely onboard. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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Open AccessArticle
Feasible Self-Calibration of Larger Field-of-View (FOV) Camera Sensors for the Advanced Driver-Assistance System (ADAS)
Sensors 2019, 19(15), 3369; https://doi.org/10.3390/s19153369
Received: 25 June 2019 / Revised: 27 July 2019 / Accepted: 29 July 2019 / Published: 31 July 2019
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Abstract
This paper proposes a self-calibration method that can be applied for multiple larger field-of-view (FOV) camera models on an advanced driver-assistance system (ADAS). Firstly, the proposed method performs a series of pre-processing steps such as edge detection, length thresholding, and edge grouping for [...] Read more.
This paper proposes a self-calibration method that can be applied for multiple larger field-of-view (FOV) camera models on an advanced driver-assistance system (ADAS). Firstly, the proposed method performs a series of pre-processing steps such as edge detection, length thresholding, and edge grouping for the segregation of robust line candidates from the pool of initial distortion line segments. A novel straightness cost constraint with a cross-entropy loss was imposed on the selected line candidates, thereby exploiting that novel loss to optimize the lens-distortion parameters using the Levenberg–Marquardt (LM) optimization approach. The best-fit distortion parameters are used for the undistortion of an image frame, thereby employing various high-end vision-based tasks on the distortion-rectified frame. In this study, an investigation was carried out on experimental approaches such as parameter sharing between multiple camera systems and model-specific empirical γ -residual rectification factor. The quantitative comparisons were carried out between the proposed method and traditional OpenCV method as well as contemporary state-of-the-art self-calibration techniques on KITTI dataset with synthetically generated distortion ranges. Famous image consistency metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and position error in salient points estimation were employed for the performance evaluations. Finally, for a better performance validation of the proposed system on a real-time ADAS platform, a pragmatic approach of qualitative analysis has been conducted through streamlining high-end vision-based tasks such as object detection, localization, and mapping, and auto-parking on undistorted frames. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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Open AccessArticle
Discovering Speed Changes of Vehicles from Audio Data
Sensors 2019, 19(14), 3067; https://doi.org/10.3390/s19143067
Received: 23 May 2019 / Revised: 1 July 2019 / Accepted: 5 July 2019 / Published: 11 July 2019
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Abstract
In this paper, we focus on detection of speed changes from audio data, representing recordings of cars passing a microphone placed near the road. The goal of this work is to observe the behavior of drivers near control points, in order to check [...] Read more.
In this paper, we focus on detection of speed changes from audio data, representing recordings of cars passing a microphone placed near the road. The goal of this work is to observe the behavior of drivers near control points, in order to check whether their driving is safe both when approaching the speed camera and after passing it. The audio data were recorded in controlled conditions, and they are publicly available for downloading. They represent one of three classes: car accelerating, decelerating, or maintaining constant speed. We used SVM, random forests, and artificial neural networks as classifiers, as well as the time series based approach. We also tested several approaches to audio data representation, namely: average values of basic audio features within the analyzed time segment, parametric description of the time evolution of these features, and parametric description of curves (lines) in the spectrogram. Additionally, the combinations of these representations were used in classification experiments. As a final step, we constructed an ensemble classifier, consisting of the best models. The proposed solution achieved an accuracy of almost 95%, without mistaking acceleration with deceleration, and very rare mistakes between stable speed and speed changes. The outcomes of this work can become a basis for campaigns aiming at improving traffic safety. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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Open AccessArticle
A Driver’s Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model
Sensors 2019, 19(12), 2670; https://doi.org/10.3390/s19122670
Received: 5 May 2019 / Revised: 8 June 2019 / Accepted: 11 June 2019 / Published: 13 June 2019
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Abstract
Lane changing is considered as one of the most dangerous driving behaviors because drivers have to deal with the traffic conflicts on both the current and target lanes. This study aimed to propose a method of predicting the driving risks during the lane-changing [...] Read more.
Lane changing is considered as one of the most dangerous driving behaviors because drivers have to deal with the traffic conflicts on both the current and target lanes. This study aimed to propose a method of predicting the driving risks during the lane-changing process using drivers’ physiology measurement data and vehicle dynamic data. All the data used in the proposed model were obtained by portable sensors with the capability of recording data in the actual driving process. A hidden Markov model (HMM) was proposed to link driving risk with drivers’ physiology information and vehicle dynamic data. The two-factor indicators were established to evaluate the performances of eye movement, heart rate variability, and vehicle dynamic parameters on driving risk. The standard deviation of normal to normal R–R intervals of the heart rate (SDNN), fixation duration, saccade range, and average speed were then selected as the input of the HMM. The HMM was trained and tested using field-observed data collected in Xi’an City. The proposed model using the data from the physiology measurement sensor can identify dangerous driving state from normal driving state and predict the transition probability between these two states. The results match the perceptions of the tested drivers with an accuracy rate of 90.67%. The proposed model can be used to develop proactive crash prevention strategies. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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Open AccessArticle
Evaluating and Diagnosing Road Intersection Operation Performance Using Floating Car Data
Sensors 2019, 19(10), 2256; https://doi.org/10.3390/s19102256
Received: 9 April 2019 / Revised: 13 May 2019 / Accepted: 13 May 2019 / Published: 15 May 2019
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Abstract
Urban road intersections play an important role in deciding the total travel time and the overall travel efficiency. In this paper, an innovative traffic grid model has been proposed, which evaluates and diagnoses the traffic status and the time delay at intersections across [...] Read more.
Urban road intersections play an important role in deciding the total travel time and the overall travel efficiency. In this paper, an innovative traffic grid model has been proposed, which evaluates and diagnoses the traffic status and the time delay at intersections across whole urban road networks. This method is grounded on a massive amount of floating car data sampled at a rate of 3 s, and it is composed of three major parts. (1) A grid model is built to transform intersections into discrete cells, and the floating car data are matched to the grids through a simple assignment process. (2) Based on the grid model, a set of key traffic parameters (e.g., the total time delay of all the directions of the intersection and the average speed of each direction) is derived. (3) Using these parameters, intersections are evaluated and the ones with the longest traffic delays are identified. The obtained intersections are further examined in terms of the traffic flow ratio and the green time ratio as well as the difference between these two variables. Using the central area of Beijing as the case study, the potential and feasibility of the proposed method are demonstrated and the unreasonable signal timing phases are detected. The developed method can be easily transferred to other cities, making it a useful and practical tool for traffic managers to evaluate and diagnose urban signal intersections as well as to design optimal measures for reducing traffic delay and increase operation efficiency at the intersections. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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Open AccessArticle
Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks
Sensors 2019, 19(10), 2229; https://doi.org/10.3390/s19102229
Received: 15 April 2019 / Revised: 9 May 2019 / Accepted: 9 May 2019 / Published: 14 May 2019
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Abstract
Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack [...] Read more.
Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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Open AccessArticle
Design and Evaluation of a Surface Electromyography-Controlled Steering Assistance Interface
Sensors 2019, 19(6), 1308; https://doi.org/10.3390/s19061308
Received: 26 January 2019 / Revised: 7 March 2019 / Accepted: 12 March 2019 / Published: 15 March 2019
PDF Full-text (3042 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Millions of drivers could experience shoulder muscle overload when rapidly rotating steering wheels and reduced steering ability at increased steering wheel angles. In order to address these issues for drivers with disability, surface electromyography (sEMG) sensors measuring biceps brachii muscle activity were incorporated [...] Read more.
Millions of drivers could experience shoulder muscle overload when rapidly rotating steering wheels and reduced steering ability at increased steering wheel angles. In order to address these issues for drivers with disability, surface electromyography (sEMG) sensors measuring biceps brachii muscle activity were incorporated into a steering assistance system for remote steering wheel rotation. The path-following accuracy of the sEMG interface with respect to a game steering wheel was evaluated through driving simulator trials. Human participants executed U-turns with differing radii of curvature. For a radius of curvature equal to the minimum vehicle turning radius of 3.6 m, the sEMG interface had significantly greater accuracy than the game steering wheel, with intertrial median lateral errors of 0.5 m and 1.2 m, respectively. For a U-turn with a radius of 7.2 m, the sEMG interface and game steering wheel were comparable in accuracy, with respective intertrial median lateral errors of 1.6 m and 1.4 m. The findings of this study could be utilized to realize accurate sEMG-controlled automobile steering for persons with disability. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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Open AccessArticle
Unconstrained Monitoring Method for Heartbeat Signals Measurement using Pressure Sensors Array
Sensors 2019, 19(2), 368; https://doi.org/10.3390/s19020368
Received: 24 November 2018 / Revised: 10 January 2019 / Accepted: 15 January 2019 / Published: 17 January 2019
PDF Full-text (6659 KB) | HTML Full-text | XML Full-text
Abstract
An unconstrained monitoring method for a driver’s heartbeat is investigated in this paper. Signal measurement was carried out by using pressure sensors array. Due to the inevitable changes of posture during driving, the monitoring place for heartbeat measurement needs to be adjusted accordingly. [...] Read more.
An unconstrained monitoring method for a driver’s heartbeat is investigated in this paper. Signal measurement was carried out by using pressure sensors array. Due to the inevitable changes of posture during driving, the monitoring place for heartbeat measurement needs to be adjusted accordingly. An experiment was conducted to attach a pressure sensors array to the backrest of a seat. On the basis of the extreme learning machine classification method, driving posture can be recognized by monitoring the distribution of pressure signals. Then, a band-pass filter in heart rate range is adapted to the pressure signals in the frequency domain. Furthermore, a peak point array of the processed pressure frequency spectrum is derived and has the same distribution as the pressure signals. Thus, the heartbeat signals can be extracted from pressure sensors. Then, the correlation coefficient analysis of heartbeat signals and electrocardio-signals is performed. The results show a high level of correlation. Finally, the effects of driving posture on heartbeat signal extraction are discussed to obtain a theoretical foundation for measuring point real-time adjustment. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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Open AccessArticle
Modeling and Control of an Active Stabilizing Assistant System for a Bicycle
Sensors 2019, 19(2), 248; https://doi.org/10.3390/s19020248
Received: 6 December 2018 / Revised: 1 January 2019 / Accepted: 4 January 2019 / Published: 10 January 2019
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Abstract
This study designs and controls an active stabilizing assistant system (ASAS) for a bicycle. Using the gyroscopic effect of two spinning flywheels, the ASAS generates torques that assist the rider to stabilize the bicycle in various riding modes. Riding performance and the rider’s [...] Read more.
This study designs and controls an active stabilizing assistant system (ASAS) for a bicycle. Using the gyroscopic effect of two spinning flywheels, the ASAS generates torques that assist the rider to stabilize the bicycle in various riding modes. Riding performance and the rider’s safety are improved. To simulate the system dynamic behavior, a model of a bicycle–rider system with the ASAS on the rear seat is developed. This model has 14 degrees of freedom and is derived using Lagrange equations. In order to evaluate the efficacy of the ASAS in interacting with the rider’s control actions, simulations of the bicycle–rider system with the ASAS are conducted. The results for the same rider for the bicycle with an ASAS and on a traditional bicycle are compared for various riding conditions. In three cases of simulation for different riding conditions, the bicycle with the proposed ASAS handles better, with fewer control actions being required than for a traditional bicycle. Full article
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transport)
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