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15 pages, 2808 KiB

Open AccessArticle

Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior

by Anna-Maria Stavrakaki, Dimitrios I. Tselentis, Emmanouil Barmpounakis, Eleni I. Vlahogianni and George Yannis

Sensors 2020, 20(9), 2600; https://doi.org/10.3390/s20092600 - 2 May 2020

Cited by 14 | Viewed by 4155

Abstract

The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific [...] Read more.

The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to process the data collected and determine the time point at which behavior converges. Detailed data collected from smartphone sensors are used to test the proposed methodology. The driving metrics used in the analysis are the number of harsh acceleration and braking events, the duration of mobile usage while driving and the percentage of time driving over the speed limits. Convergence was tested in terms of both the magnitude and volatility of each metric for different trips and analysis is performed for several trip durations. Results indicated that there is no specific time point or number of trips after which driving behavior stabilizes for all drivers and/or all metrics examined. The driving behavior stabilization is mostly affected by the duration of the trips examined and the aggressiveness of the driver. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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20 pages, 1605 KiB

Open AccessArticle

Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model

by Hasan A.H. Naji, Qingji Xue, Ke Zheng and Nengchao Lyu

Sensors 2020, 20(8), 2331; https://doi.org/10.3390/s20082331 - 19 Apr 2020

Cited by 17 | Viewed by 3428

Abstract

Driving risk varies substantially according to many factors related to the driven vehicle, environmental conditions, and drivers. This study explores the contributing historical factors of driving risk with hierarchical clustering analysis and the quasi-Poisson regression model. The dataset of the study was collected [...] Read more.

Driving risk varies substantially according to many factors related to the driven vehicle, environmental conditions, and drivers. This study explores the contributing historical factors of driving risk with hierarchical clustering analysis and the quasi-Poisson regression model. The dataset of the study was collected from two sources: naturalistic driving experiments and self-reports. The drivers who participated in the naturalistic driving experiment were categorized into four risk groups according to their near-crash frequency with the hierarchical clustering method. Moreover, a quasi-Poisson model was used to identify the essential factors of individual driving risk. The findings of this study indicated that historical driving factors have substantial impacts on individual risk of drivers. These factors include the total number of miles driven, the driver’s age, the number of illegal parking (past three years), the number of over-speeding (past three years) and passing red lights (past three years). The outcome of the study can help transportation officials, educators, and researchers to consider the influencing factors on individual driving risk and can give insights and provide suggestions to improve driving safety. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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29 pages, 5044 KiB

Open AccessArticle

A Formal and Quantifiable Log Analysis Framework for Test Driving of Autonomous Vehicles

by Kyungbok Sung, Kyoung-Wook Min, Jeongdan Choi and Byung-Cheol Kim

Sensors 2020, 20(5), 1356; https://doi.org/10.3390/s20051356 - 2 Mar 2020

Cited by 4 | Viewed by 8664

Abstract

We propose a log analysis framework for test driving of autonomous vehicles. The log of a vehicle is a fundamental source to detect and analyze events during driving. A set of dumped logs are, however, usually mixed and fragmented since they are generated [...] Read more.

We propose a log analysis framework for test driving of autonomous vehicles. The log of a vehicle is a fundamental source to detect and analyze events during driving. A set of dumped logs are, however, usually mixed and fragmented since they are generated concurrently by a number of modules such as sensors, actuators and programs. This makes it hard to analyze them to discover latent errors that could occur due to complex chain reactions among those modules. Our framework provides a logging architecture based on formal specifications, which hierarchically organizes them to find out a priori relationships between them. Then, algorithmic or implementation errors can be detected by examining a posteriori relationships. However, a test in a situation of certain parameters, so called an oracle test, does not necessarily trigger latent violations of the relationships. In our framework, this is remedied by adopting metamorphic testing to quantitatively verify the formal specification. As a working proof, we define three metamorphic relations critical for testing autonomous vehicles and verify them in a quantitative manner based on our logging system. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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26 pages, 7740 KiB

Open AccessArticle

A Self-Calibrating Probabilistic Framework for 3D Environment Perception Using Monocular Vision

by Razvan Itu and Radu Gabriel Danescu

Sensors 2020, 20(5), 1280; https://doi.org/10.3390/s20051280 - 27 Feb 2020

Cited by 10 | Viewed by 3845

Abstract

Cameras are sensors that are available anywhere and to everyone, and can be placed easily inside vehicles. While stereovision setups of two or more synchronized cameras have the advantage of directly extracting 3D information, a single camera can be easily set up behind [...] Read more.

Cameras are sensors that are available anywhere and to everyone, and can be placed easily inside vehicles. While stereovision setups of two or more synchronized cameras have the advantage of directly extracting 3D information, a single camera can be easily set up behind the windshield (like a dashcam), or above the dashboard, usually as an internal camera of a mobile phone placed there for navigation assistance. This paper presents a framework for extracting and tracking obstacle 3D data from the surrounding environment of a vehicle in traffic, using as a sensor a generic camera. The system combines the strength of Convolutional Neural Network (CNN)-based segmentation with a generic probabilistic model of the environment, the dynamic occupancy grid. The main contributions presented in this paper are the following: A method for generating the probabilistic measurement model from monocular images, based on CNN segmentation, which takes into account the particularities, uncertainties, and limitations of monocular vision; a method for automatic calibration of the extrinsic and intrinsic parameters of the camera, without the need of user assistance; the integration of automatic calibration and measurement model generation into a scene tracking system that is able to work with any camera to perceive the obstacles in real traffic. The presented system can be easily fitted to any vehicle, working standalone or together with other sensors, to enhance the environment perception capabilities and improve the traffic safety. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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33 pages, 11126 KiB

Open AccessArticle

Stabilization and Validation of 3D Object Position Using Multimodal Sensor Fusion and Semantic Segmentation

by Mircea Paul Muresan, Ion Giosan and Sergiu Nedevschi

Sensors 2020, 20(4), 1110; https://doi.org/10.3390/s20041110 - 18 Feb 2020

Cited by 70 | Viewed by 8595

Abstract

The stabilization and validation process of the measured position of objects is an important step for high-level perception functions and for the correct processing of sensory data. The goal of this process is to detect and handle inconsistencies between different sensor measurements, which [...] Read more.

The stabilization and validation process of the measured position of objects is an important step for high-level perception functions and for the correct processing of sensory data. The goal of this process is to detect and handle inconsistencies between different sensor measurements, which result from the perception system. The aggregation of the detections from different sensors consists in the combination of the sensorial data in one common reference frame for each identified object, leading to the creation of a super-sensor. The result of the data aggregation may end up with errors such as false detections, misplaced object cuboids or an incorrect number of objects in the scene. The stabilization and validation process is focused on mitigating these problems. The current paper proposes four contributions for solving the stabilization and validation task, for autonomous vehicles, using the following sensors: trifocal camera, fisheye camera, long-range RADAR (Radio detection and ranging), and 4-layer and 16-layer LIDARs (Light Detection and Ranging). We propose two original data association methods used in the sensor fusion and tracking processes. The first data association algorithm is created for tracking LIDAR objects and combines multiple appearance and motion features in order to exploit the available information for road objects. The second novel data association algorithm is designed for trifocal camera objects and has the objective of finding measurement correspondences to sensor fused objects such that the super-sensor data are enriched by adding the semantic class information. The implemented trifocal object association solution uses a novel polar association scheme combined with a decision tree to find the best hypothesis–measurement correlations. Another contribution we propose for stabilizing object position and unpredictable behavior of road objects, provided by multiple types of complementary sensors, is the use of a fusion approach based on the Unscented Kalman Filter and a single-layer perceptron. The last novel contribution is related to the validation of the 3D object position, which is solved using a fuzzy logic technique combined with a semantic segmentation image. The proposed algorithms have a real-time performance, achieving a cumulative running time of 90 ms, and have been evaluated using ground truth data extracted from a high-precision GPS (global positioning system) with 2 cm accuracy, obtaining an average error of 0.8 m. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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17 pages, 380 KiB

Open AccessArticle

Impact of Leadership and Mobility on Consensus-Building in Sensor Networks

by Roya Norouzi Kandalan, Murali Varanasi, Bill Buckles and Kamesh Namuduri

Sensors 2020, 20(4), 1081; https://doi.org/10.3390/s20041081 - 17 Feb 2020

Cited by 1 | Viewed by 2624

Abstract

Introducing leadership and mobility is known to benefit wireless sensor networks in terms of consensus-building and collective decision-making. However, these benefits are neither analytically proven nor quantified in the literature. This paper fills this gap by investigating the mobility dynamics in wireless sensor [...] Read more.

Introducing leadership and mobility is known to benefit wireless sensor networks in terms of consensus-building and collective decision-making. However, these benefits are neither analytically proven nor quantified in the literature. This paper fills this gap by investigating the mobility dynamics in wireless sensor networks analytically. The results of the analytical investigation are presented as a set of theorems and their proofs. This paper also establishes a natural synergy between the leader-follower model and its bipartite graph representation. It demonstrates the advantages of the leader-follower model for consensus-building over others in terms of improved convergence rate. It presents a strategy for choosing leaders from among the agents participating in the consensus-building process using the well-known graph-coloring solution. Then, it shows how the leader-follower model helps improve the convergence rate of consensus-building. Finally, it shows that the convergence rate of the consensus-building process can be further improved by making the leaders mobile. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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26 pages, 8115 KiB

Open AccessArticle

Integrated Avoid Collision Control of Autonomous Vehicle Based on Trajectory Re-Planning and V2V Information Interaction

by Fen Lin, Kaizheng Wang, Youqun Zhao and Shaobo Wang

Sensors 2020, 20(4), 1079; https://doi.org/10.3390/s20041079 - 17 Feb 2020

Cited by 20 | Viewed by 4225

Abstract

An integrated longitudinal-lateral control method is proposed for autonomous vehicle trajectory tracking and dynamic collision avoidance. A method of obstacle trajectory prediction is proposed, in which the trajectory of the obstacle is predicted and the dynamic solution of the reference trajectory is realized. [...] Read more.

An integrated longitudinal-lateral control method is proposed for autonomous vehicle trajectory tracking and dynamic collision avoidance. A method of obstacle trajectory prediction is proposed, in which the trajectory of the obstacle is predicted and the dynamic solution of the reference trajectory is realized. Aiming at the lane changing scene of autonomous vehicles driving in the same direction and adjacent lanes, a trajectory re-planning motion controller with the penalty function is designed. The reference trajectory parameterized output of local reprogramming is realized by using the method of curve fitting. In the framework of integrated control, Fuzzy adaptive (proportional-integral) PI controller is proposed for longitudinal velocity tracking. The selection and control of controller and velocity are realized by logical threshold method; A model predictive control (MPC) with vehicle-to-vehicle (V2V) information interaction modular and the driver characteristics is proposed for direction control. According to the control target, the objective function and constraints of the controller are designed. The proposed method’s performance in different scenarios is verified by simulation. The results show that the autonomous vehicles can avoid collision and have good stability. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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32 pages, 13637 KiB

Open AccessArticle

Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System

by Yeonseok Park, Anthony Choi and Keonwook Kim

Sensors 2020, 20(3), 925; https://doi.org/10.3390/s20030925 - 10 Feb 2020

Cited by 8 | Viewed by 3370

Abstract

Vehicle-mounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The three-dimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the received signal, the flight times between microphones [...] Read more.

Vehicle-mounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The three-dimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the received signal, the flight times between microphones delivers the essential information to locate the sound source. This paper proposes a novel method to design a sound localization system based on the single analog microphone network. This article involves the flight time estimation for two microphones with non-parametric homomorphic deconvolution. The parametric methods are also suggested with Yule-walker, Prony, and Steiglitz-McBride algorithm to derive the coefficient values of the propagation model for flight time estimation. The non-parametric and Steiglitz-McBride method demonstrated significantly low bias and variance for 20 or higher ensemble average length. The Yule-walker and Prony algorithms showed gradually improved statistical performance for increased ensemble average length. Hence, the non-parametric and parametric homomorphic deconvolution well represent the flight time information. The derived non-parametric and parametric output with distinct length will serve as the featured information for a complete localization system based on machine learning or deep learning in future works. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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17 pages, 6966 KiB

Open AccessArticle

Surface Electromyography-Controlled Automobile Steering Assistance

by Edric John Cruz Nacpil and Kimihiko Nakano

Sensors 2020, 20(3), 809; https://doi.org/10.3390/s20030809 - 2 Feb 2020

Cited by 9 | Viewed by 5809

Abstract

Disabilities of the upper limb, such as hemiplegia or upper limb amputation, can limit automobile drivers to steering with one healthy arm. For the benefit of these drivers, recent studies have developed prototype interfaces that realized surface electromyography (sEMG)-controlled steering assistance with path-following [...] Read more.

Disabilities of the upper limb, such as hemiplegia or upper limb amputation, can limit automobile drivers to steering with one healthy arm. For the benefit of these drivers, recent studies have developed prototype interfaces that realized surface electromyography (sEMG)-controlled steering assistance with path-following accuracy that has been validated with driving simulations. In contrast, the current study expands the application of sEMG-controlled steering assistance by validating the Myo armband, a mass-produced sEMG-based interface, with respect to the path-following accuracy of a commercially available automobile. It was hypothesized that one-handed remote steering with the Myo armband would be comparable or superior to the conventional operation of the automobile steering wheel. Although results of low-speed field testing indicate that the Myo armband had lower path-following accuracy than the steering wheel during a 90° turn and wide U-turn at twice the minimum turning radius, the Myo armband had superior path-following accuracy for a narrow U-turn at the minimum turning radius and a 45° turn. Given its overall comparability to the steering wheel, the Myo armband could be feasibly applied in future automobile studies. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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17 pages, 2596 KiB

Open AccessArticle

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

by Eldar Šabanovič, Vidas Žuraulis, Olegas Prentkovskis and Viktor Skrickij

Sensors 2020, 20(3), 612; https://doi.org/10.3390/s20030612 - 22 Jan 2020

Cited by 111 | Viewed by 8905

Abstract

Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems [...] Read more.

Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stability-related solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the anti-lock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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21 pages, 9676 KiB

Open AccessArticle

Recognizing New Classes with Synthetic Data in the Loop: Application to Traffic Sign Recognition

by Gabriel Villalonga, Joost Van de Weijer and Antonio M. López

Sensors 2020, 20(3), 583; https://doi.org/10.3390/s20030583 - 21 Jan 2020

Cited by 8 | Viewed by 3399

Abstract

On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new [...] Read more.

On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio

\sim 1 / 4

for new/known classes; even for more challenging ratios such as

\sim 4 / 1

, the results are also very positive. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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18 pages, 4615 KiB

Open AccessArticle

Finger Gesture Spotting from Long Sequences Based on Multi-Stream Recurrent Neural Networks

by Gibran Benitez-Garcia, Muhammad Haris, Yoshiyuki Tsuda and Norimichi Ukita

Sensors 2020, 20(2), 528; https://doi.org/10.3390/s20020528 - 18 Jan 2020

Cited by 9 | Viewed by 3672

Abstract

Gesture spotting is an essential task for recognizing finger gestures used to control in-car touchless interfaces. Automated methods to achieve this task require to detect video segments where gestures are observed, to discard natural behaviors of users’ hands that may look as target [...] Read more.

Gesture spotting is an essential task for recognizing finger gestures used to control in-car touchless interfaces. Automated methods to achieve this task require to detect video segments where gestures are observed, to discard natural behaviors of users’ hands that may look as target gestures, and be able to work online. In this paper, we address these challenges with a recurrent neural architecture for online finger gesture spotting. We propose a multi-stream network merging hand and hand-location features, which help to discriminate target gestures from natural movements of the hand, since these may not happen in the same 3D spatial location. Our multi-stream recurrent neural network (RNN) recurrently learns semantic information, allowing to spot gestures online in long untrimmed video sequences. In order to validate our method, we collect a finger gesture dataset in an in-vehicle scenario of an autonomous car. 226 videos with more than 2100 continuous instances were captured with a depth sensor. On this dataset, our gesture spotting approach outperforms state-of-the-art methods with an improvement of about 10% and 15% of recall and precision, respectively. Furthermore, we demonstrated that by combining with an existing gesture classifier (a 3D Convolutional Neural Network), our proposal achieves better performance than previous hand gesture recognition methods. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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16 pages, 7894 KiB

Open AccessArticle

Deep-Learning-Based Real-Time Road Traffic Prediction Using Long-Term Evolution Access Data

by Byoungsuk Ji and Ellen J. Hong

Sensors 2019, 19(23), 5327; https://doi.org/10.3390/s19235327 - 3 Dec 2019

Cited by 16 | Viewed by 5754

Abstract

In this paper, we propose a method for deep-learning-based real-time road traffic predictions using long-term evolution (LTE) access data. The proposed system generates a road traffic speed learning model based on road speed data and historical LTE data collected from a plurality of [...] Read more.

In this paper, we propose a method for deep-learning-based real-time road traffic predictions using long-term evolution (LTE) access data. The proposed system generates a road traffic speed learning model based on road speed data and historical LTE data collected from a plurality of base stations located within a predetermined radius from the road. Real-time LTE data were the input for the generated learning model in order to predict the real-time speed of traffic. Since the system was developed using a time-series-based road traffic speed learning model based on LTE data from the past, it is possible for it to be used for a road where the environment has changed. Moreover, even on roads where the collection of traffic data is invalid, such as a radio shadow area, it is possible to directly enter real-time wireless communications data into the traffic speed learning model to predict the traffic speed on the road in real time, and in turn, raise the accuracy of real-time road traffic predictions. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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16 pages, 4621 KiB

Open AccessArticle

Vehicle Speed and Length Estimation Errors Using the Intelligent Transportation System with a Set of Anisotropic Magneto-Resistive (AMR) Sensors

by Vytautas Markevicius, Dangirutis Navikas, Adam Idzkowski, Donatas Miklusis, Darius Andriukaitis, Algimantas Valinevicius, Mindaugas Zilys, Mindaugas Cepenas and Wojciech Walendziuk

Sensors 2019, 19(23), 5234; https://doi.org/10.3390/s19235234 - 28 Nov 2019

Cited by 11 | Viewed by 4370

Abstract

Seeking an effective method for estimating the speed and length of a car is still a challenge for engineers and scientists who work on intelligent transportation systems. This paper focuses on a self-developed system equipped with four anisotropic magneto-resistive (AMR) sensors which are [...] Read more.

Seeking an effective method for estimating the speed and length of a car is still a challenge for engineers and scientists who work on intelligent transportation systems. This paper focuses on a self-developed system equipped with four anisotropic magneto-resistive (AMR) sensors which are placed on a road lane. The piezoelectric polyvinylidene fluoride (PVDF) sensors are also mounted and used as a reference device. The methods applied in the research are well-known: the fixed threshold-based method and the adaptive two-extreme-peak detection method. However, the improved accuracy of estimating the length by using one of the methods, which is based on computing the difference quotient of a time-discrete signal (representing the changes in the magnitude of the magnetic field of the Earth), is observed. The obtained results, i.e., the speed and length of a vehicle, are presented for various values of the increment Δn used in numerical differentiation of magnetic field magnitude data. The results were achieved in real traffic conditions after analyzing a data set M = 290 of vehicle signatures. The accuracy was evaluated by calculating MAE (Mean Absolute Error), RMSE (Root Mean Squared Error) for different classes of vehicles. The MAE is within the range of 0.52 m–1.18 m when using the appropriate calibration factor. The results are dependent on the distance between sensors, the speed of vehicle and the signal processing method applied. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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16 pages, 5332 KiB

Open AccessArticle

Estimating Driving Fatigue at a Plateau Area with Frequent and Rapid Altitude Change

by Fan Wang, Hong Chen, Cai-hua Zhu, Si-rui Nan and Yan Li

Sensors 2019, 19(22), 4982; https://doi.org/10.3390/s19224982 - 15 Nov 2019

Cited by 12 | Viewed by 3511

Abstract

Due to the influence of altitude change on a driver’s heart rate, it is difficult to estimate driving fatigue using heart rate variability (HRV) at a road segment with frequent and rapid altitude change. Accordingly, a novel method of driving fatigue estimation for [...] Read more.

Due to the influence of altitude change on a driver’s heart rate, it is difficult to estimate driving fatigue using heart rate variability (HRV) at a road segment with frequent and rapid altitude change. Accordingly, a novel method of driving fatigue estimation for driving at plateau area with frequent altitude changes is proposed to provide active safety monitoring in real time. A naturalistic driving experiment at Qinghai-Tibet highway was conducted to collect drivers’ electrocardiogram data and eye movement data. The results of the eye movement-based method were selected to enhance the HRV-based driving fatigue degree estimation method. A correction factor was proposed to correct the HRV-based method at the plateau area so that the estimation can be made via common portable devices. The correction factors for both upslope and downslope segments were estimated using the field experiment data. The results on the estimation of revised driving fatigue degree can describe the driver’s fatigue status accurately for all the road segments at the plateau area with altitudes from 3540 to 4767 m. The results can provide theoretical references for the design of the devices of active safety prevention. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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15 pages, 5307 KiB

Open AccessArticle

Virtual Current Sensor in the Fault-Tolerant Field-Oriented Control Structure of an Induction Motor Drive

by Michal Adamczyk and Teresa Orlowska-Kowalska

Sensors 2019, 19(22), 4979; https://doi.org/10.3390/s19224979 - 15 Nov 2019

Cited by 40 | Viewed by 4266

Abstract

Designing electrical drives resistant to the failures of chosen sensors has recently become increasingly popular due to the possibility of their use in fault-tolerant control (FTC) systems including drives for electric vehicles. In this article, a virtual current sensor (VCS) based on an [...] Read more.

Designing electrical drives resistant to the failures of chosen sensors has recently become increasingly popular due to the possibility of their use in fault-tolerant control (FTC) systems including drives for electric vehicles. In this article, a virtual current sensor (VCS) based on an algorithmic method for the reconstruction of the induction motor (IM) phase currents after current sensor faults was proposed. This stator current estimator is based only on the measurements of the DC-bus voltage in the intermediate circuit of the voltage-source inverter (VSI) and a rotor speed. This proposal is dedicated to fault-tolerant vector controlled IM drives, where it is necessary to switch to scalar control as a result of damage to the current sensors. The proposed VCS allows further uninterrupted operation of the direct rotor-field oriented control (DRFOC) of the induction motor drive. The stator current estimator has been presented in the form of equations, enabling its practical implementation in a microprocessor system. Simulation studies of the proposed algorithm in an open and closed-loop DRFOC structure are presented under different operation conditions of the drive system. The experimental verification of the proposed method is also presented and the accuracy of the stator current estimation algorithm is analyzed under various operating conditions of the drive system. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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14 pages, 2508 KiB

Open AccessArticle

Traffic Speed Prediction: An Attention-Based Method

by Duanyang Liu, Longfeng Tang, Guojiang Shen and Xiao Han

Sensors 2019, 19(18), 3836; https://doi.org/10.3390/s19183836 - 5 Sep 2019

Cited by 26 | Viewed by 3945

Abstract

Short-term traffic speed prediction has become one of the most important parts of intelligent transportation systems (ITSs). In recent years, deep learning methods have demonstrated their superiority both in accuracy and efficiency. However, most of them only consider the temporal information, overlooking the [...] Read more.

Short-term traffic speed prediction has become one of the most important parts of intelligent transportation systems (ITSs). In recent years, deep learning methods have demonstrated their superiority both in accuracy and efficiency. However, most of them only consider the temporal information, overlooking the spatial or some environmental factors, especially the different correlations between the target road and the surrounding roads. This paper proposes a traffic speed prediction approach based on temporal clustering and hierarchical attention (TCHA) to address the above issues. We apply temporal clustering to the target road to distinguish the traffic environment. Traffic data in each cluster have a similar distribution, which can help improve the prediction accuracy. A hierarchical attention-based mechanism is then used to extract the features at each time step. The encoder measures the importance of spatial features, and the decoder measures the temporal ones. The proposed method is evaluated over the data of a certain area in Hangzhou, and experiments have shown that this method can outperform the state of the art for traffic speed prediction. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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15 pages, 3983 KiB

Open AccessArticle

A New Switched State Jump Observer for Traffic Density Estimation in Expressways Based on Hybrid-Dynamic-Traffic-Network-Model

by Wenbin Zha, Yuqi Guo, Huawei Wu, Miguel Angel Sotelo, Yulin Ma, Qian Yi, Zhixiong Li and Xin Sun

Sensors 2019, 19(18), 3822; https://doi.org/10.3390/s19183822 - 4 Sep 2019

Cited by 7 | Viewed by 2726

Abstract

When faced with problems such as traffic state estimation, state prediction, and congestion identification for the expressway network, a novel switched observer design strategy with jump states is required to reconstruct the traffic scene more realistically. In this study, the expressway network is [...] Read more.

When faced with problems such as traffic state estimation, state prediction, and congestion identification for the expressway network, a novel switched observer design strategy with jump states is required to reconstruct the traffic scene more realistically. In this study, the expressway network is firstly modeled as the special discrete switched system, which is called the piecewise affine system model, a partition of state subspace is introduced, and the convex polytopes are utilized to describe the combination modes of cells. Secondly, based on the hybrid dynamic traffic network model, the corresponding switched observer (including state jumps) is designed. Furthermore, by applying multiple Lyapunov functions and S-procedure theory, the observer design problem can be converted into the existence issue of the solutions to the linear matrix inequality. As a result, a set of gain matrices can be obtained. The estimated states start to jump when the mode changes occur, and the updated value of the estimated state mainly depends on the estimated and the measured values at the previous time. Lastly, the designed state jump observer is applied to the Beijing Jingkai expressway, and the superiority and the feasibility are demonstrated in the application results. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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18 pages, 9088 KiB

Open AccessArticle

Prototyping a System for Truck Differential Lock Control

by Pavel Kučera and Václav Píštěk

Sensors 2019, 19(16), 3619; https://doi.org/10.3390/s19163619 - 20 Aug 2019

Cited by 14 | Viewed by 4803

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 Transportation)

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18 pages, 4968 KiB

Open AccessArticle

Modeling and Control of a Six Degrees of Freedom Maglev Vibration Isolation System

by Qianqian Wu, Ning Cui, Sifang Zhao, Hongbo Zhang and Bilong Liu

Sensors 2019, 19(16), 3608; https://doi.org/10.3390/s19163608 - 19 Aug 2019

Cited by 15 | Viewed by 4105

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 Transportation)

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25 pages, 5125 KiB

Open AccessArticle

Sensorless Control of the Permanent Magnet Synchronous Motor

by Konrad Urbanski and Dariusz Janiszewski

Sensors 2019, 19(16), 3546; https://doi.org/10.3390/s19163546 - 14 Aug 2019

Cited by 43 | Viewed by 8582

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 Transportation)

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17 pages, 1199 KiB

Open AccessArticle

Autonomous Vision-Based Aerial Grasping for Rotorcraft Unmanned Aerial Vehicles

by Lishan Lin, Yuji Yang, Hui Cheng and Xuechen Chen

Sensors 2019, 19(15), 3410; https://doi.org/10.3390/s19153410 - 3 Aug 2019

Cited by 21 | Viewed by 5487

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 Transportation)

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29 pages, 104784 KiB

Open AccessArticle

Feasible Self-Calibration of Larger Field-of-View (FOV) Camera Sensors for the Advanced Driver-Assistance System (ADAS)

by Vijay Kakani, Hakil Kim, Mahendar Kumbham, Donghun Park, Cheng-Bin Jin and Van Huan Nguyen

Sensors 2019, 19(15), 3369; https://doi.org/10.3390/s19153369 - 31 Jul 2019

Cited by 20 | Viewed by 8540

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 Transportation)

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22 pages, 2500 KiB

Open AccessArticle

Discovering Speed Changes of Vehicles from Audio Data

by Elżbieta Kubera, Alicja Wieczorkowska, Andrzej Kuranc and Tomasz Słowik

Sensors 2019, 19(14), 3067; https://doi.org/10.3390/s19143067 - 11 Jul 2019

Cited by 21 | Viewed by 4005

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 Transportation)

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21 pages, 4702 KiB

Open AccessArticle

A Driver’s Physiology Sensor-Based Driving Risk Prediction Method for Lane-Changing Process Using Hidden Markov Model

by Yan Li, Fan Wang, Hui Ke, Li-li Wang and Cheng-cheng Xu

Sensors 2019, 19(12), 2670; https://doi.org/10.3390/s19122670 - 13 Jun 2019

Cited by 37 | Viewed by 4691

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 Transportation)

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20 pages, 6730 KiB

Open AccessArticle

Evaluating and Diagnosing Road Intersection Operation Performance Using Floating Car Data

by Deqi Chen, Xuedong Yan, Feng Liu, Xiaobing Liu, Liwei Wang and Jiechao Zhang

Sensors 2019, 19(10), 2256; https://doi.org/10.3390/s19102256 - 15 May 2019

Cited by 17 | Viewed by 4358

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 Transportation)

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19 pages, 834 KiB

Open AccessArticle

Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

by Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li and Jianjun Hu

Sensors 2019, 19(10), 2229; https://doi.org/10.3390/s19102229 - 14 May 2019

Cited by 83 | Viewed by 7365

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 Transportation)

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20 pages, 3042 KiB

Open AccessArticle

Design and Evaluation of a Surface Electromyography-Controlled Steering Assistance Interface

by Edric John Cruz Nacpil, Zheng Wang, Rencheng Zheng, Tsutomu Kaizuka and Kimihiko Nakano

Sensors 2019, 19(6), 1308; https://doi.org/10.3390/s19061308 - 15 Mar 2019

Cited by 6 | Viewed by 6121

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 Transportation)

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14 pages, 6659 KiB

Open AccessArticle

Unconstrained Monitoring Method for Heartbeat Signals Measurement using Pressure Sensors Array

by Yongxiang Jiang, Sanpeng Deng, Hongchang Sun and Yuming Qi

Sensors 2019, 19(2), 368; https://doi.org/10.3390/s19020368 - 17 Jan 2019

Cited by 6 | Viewed by 4107

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 Transportation)

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21 pages, 9150 KiB

Open AccessArticle

Modeling and Control of an Active Stabilizing Assistant System for a Bicycle

by Chih-Keng Chen, Trung-Dung Chu and Xiao-Dong Zhang

Sensors 2019, 19(2), 248; https://doi.org/10.3390/s19020248 - 10 Jan 2019

Cited by 20 | Viewed by 6602

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 Transportation)

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Review

Jump to: Research

26 pages, 2310 KiB

Open AccessReview

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

by Xianjian Jin, Guodong Yin and Nan Chen

Sensors 2019, 19(19), 4289; https://doi.org/10.3390/s19194289 - 3 Oct 2019

Cited by 95 | Viewed by 11027

Abstract

In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected [...] Read more.

In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided. Full article

(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)

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