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Advanced Sensing and Safety Control for Connected and Automated Vehicles

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

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 26927

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Dr. Chao Huang

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Guest Editor

Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: human–machine collaborative control; decision making; path planning; fault-tolerant control with the application of automated vehicles
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Dr. Yafei Wang

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Guest Editor

Institute of Intelligent Vehicle, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
Interests: vehicle dynamics and control; connected and automated vehicles
Special Issues, Collections and Topics in MDPI journals

Dr. Peng Hang

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Guest Editor

School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: decision-making; autonomous driving; game theory; path tracking and control
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Prof. Dr. Zhiqiang Zuo

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Guest Editor

School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: autonomous driving; network security and privacy protection; complex networks and multi-agent systems; robust control; antenna servo system control
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Dr. Bo Leng

E-Mail Website
Guest Editor

School of Automotive Studies, Tongji University, Shanghai 201804, China
Interests: vehicle state estimation; dynamics control for autonomous vehicles
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Special Issue Information

Dear Colleagues,

Connected and automated vehicle (CAV) is a transformative technology that is expected to change and improve the safety and efficiency of the mobilities. As main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hot spot in recent years. Thanks to the improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, and navigate themselves and interact with other surrounding vehicles in the dynamic environment. Further, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even their mental states can also be recognized.

The objective of this Special Issue is to compile recent research and development efforts contributing to advances in sensing and control for CAVs. The Special Issue will also welcome contributions addressing the state-of-the-art in associated developments and methodologies, and the perspectives on future developments and applications. The topics of interest within the scope of this Special Section include (but are not limited to) the following:

Sensing technologies for environment perception of CAVs;
Sensing technologies for localization and navigation of CAVs;
Sensor fusion and signal processing in CAVs;
Sensing for human behavior recognition in CAVs;
Advanced control algorithms for CAVs;
Control-oriented modeling for CAVs;
Decision making, path planning, and tracking of CAVs;
Human–automation collaboration for CAVs;
Sensing, control, and testing for safety and security of CAVs;
Emergency obstacle avoidance control of CAVs;
Eco-driving control of CAVs;
Active steering control of CAVs;
Fault diagnosis and fault-tolerant control of CAVs;
Sensing and control for multimodal vehicles (e.g., ground, aerial, underwater).

Dr. Chao Huang
Dr. Yafei Wang
Dr. Peng Hang
Prof. Dr. Zhiqiang Zuo
Dr. Bo Leng
Guest Editors

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Keywords

connected and automated vehicle (CAV)
sensing technologies
advanced control algorithms
decision making, path planning, and tracking
human-machine collaboration
emergency obstacle avoidance control, fault diagnosis and fault-tolerant control

Related Special Issue

Advanced Sensing and Safety Control for Connected and Automated Vehicles: Volume Ⅱ in Sensors (11 articles)

Published Papers (12 papers)

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Editorial

Jump to: Research

4 pages, 173 KiB

Open AccessEditorial

Advanced Sensing and Safety Control for Connected and Automated Vehicles

by Chao Huang, Yafei Wang, Peng Hang, Zhiqiang Zuo and Bo Leng

Sensors 2023, 23(2), 1037; https://doi.org/10.3390/s23021037 - 16 Jan 2023

Viewed by 1494

Abstract

The connected and automated vehicle (CAV) is a promising technology, anticipated to enhance the safety and effectiveness of mobility [...] Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles)

Research

Jump to: Editorial

17 pages, 523 KiB

Open AccessArticle

MALS-Net: A Multi-Head Attention-Based LSTM Sequence-to-Sequence Network for Socio-Temporal Interaction Modelling and Trajectory Prediction

by Fuad Hasan and Hailong Huang

Sensors 2023, 23(1), 530; https://doi.org/10.3390/s23010530 - 03 Jan 2023

Cited by 11 | Viewed by 3738

Abstract

Predicting the trajectories of surrounding vehicles is an essential task in autonomous driving, especially in a highway setting, where minor deviations in motion can cause serious road accidents. The future trajectory prediction is often not only based on historical trajectories but also on a representation of the interaction between neighbouring vehicles. Current state-of-the-art methods have extensively utilized RNNs, CNNs and GNNs to model this interaction and predict future trajectories, relying on a very popular dataset known as NGSIM, which, however, has been criticized for being noisy and prone to overfitting issues. Moreover, transformers, which gained popularity from their benchmark performance in various NLP tasks, have hardly been explored in this problem, presumably due to the accumulative errors in their autoregressive decoding nature of time-series forecasting. Therefore, we propose MALS-Net, a Multi-Head Attention-based LSTM Sequence-to-Sequence model that makes use of the transformer’s mechanism without suffering from accumulative errors by utilizing an attention-based LSTM encoder-decoder architecture. The proposed model was then evaluated in BLVD, a more practical dataset without the overfitting issue of NGSIM. Compared to other relevant approaches, our model exhibits state-of-the-art performance for both short and long-term prediction. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles)

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15 pages, 4573 KiB

Open AccessArticle

Up-Sampling Method for Low-Resolution LiDAR Point Cloud to Enhance 3D Object Detection in an Autonomous Driving Environment

by Jihwan You and Young-Keun Kim

Sensors 2023, 23(1), 322; https://doi.org/10.3390/s23010322 - 28 Dec 2022

Cited by 8 | Viewed by 3288

Abstract

Automobile datasets for 3D object detection are typically obtained using expensive high-resolution rotating LiDAR with 64 or more channels (Chs). However, the research budget may be limited such that only a low-resolution LiDAR of 32-Ch or lower can be used. The lower the resolution of the point cloud, the lower the detection accuracy. This study proposes a simple and effective method to up-sample low-resolution point cloud input that enhances the 3D object detection output by reconstructing objects in the sparse point cloud data to produce more dense data. First, the 3D point cloud dataset is converted into a 2D range image with four channels: x, y, z, and intensity. The interpolation on the empty space is calculated based on both the pixel distance and range values of six neighbor points to conserve the shapes of the original object during the reconstruction process. This method solves the over-smoothing problem faced by the conventional interpolation methods, and improves the operational speed and object detection performance when compared to the recent deep-learning-based super-resolution methods. Furthermore, the effectiveness of the up-sampling method on the 3D detection was validated by applying it to baseline 32-Ch point cloud data, which were then selected as the input to a point-pillar detection model. The 3D object detection result on the KITTI dataset demonstrates that the proposed method could increase the mAP (mean average precision) of pedestrians, cyclists, and cars by 9.2%p, 6.3%p, and 5.9%p, respectively, when compared to the baseline of the low-resolution 32-Ch LiDAR input. In future works, various dataset environments apart from autonomous driving will be analyzed. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles)

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20 pages, 2681 KiB

Open AccessArticle

Driver Assisted Lane Keeping with Conflict Management Using Robust Sliding Mode Controller

by Gabriele Perozzi, Mohamed Radjeb Oudainia, Chouki Sentouh, Jean-Christophe Popieul and Jagat Jyoti Rath

Sensors 2023, 23(1), 4; https://doi.org/10.3390/s23010004 - 20 Dec 2022

Cited by 2 | Viewed by 1384

Abstract

Lane-keeping assistance design for road vehicles is a multi-objective design problem that needs to simultaneously maintain lane tracking, ensure driver comfort, provide vehicle stability, and minimize conflict between the driver and the autonomous controller. In this work, a cooperative control strategy is proposed for lane-keeping keeping by integrating driving monitoring, variable level of assistance allocation, and human-in-the-loop control. In the first stage, a time-varying physical driver loading pattern is identified based on a relationship between lateral acceleration, road curvature, and the measured maximum driver torque. Together with the monitored driver state that indicates driver mental loading, an adaptive driver activity function is then formulated that replicates the levels of assistance required for the driver in the next stage. To smoothly transition authority between various modes (from manual to autonomous and vice versa) based on the generated levels of assistance, a novel higher-order sliding mode controller is proposed and closed-loop stability is established. Further, a novel sharing parameter (which is proportional to the torques coming from the driver and from the autonomous controller) is used to minimize the conflict. Experimental results on the SHERPA high-fidelity vehicle simulator show the real-time implementation feasibility. Extensive experimental results provided on the Satory test track show improvement in cooperative driving quality by 9.4%, reduction in steering workload by 86.13%, and reduced conflict by 65.38% when compared with the existing design (no sharing parameter). These results on the cooperative performance highlight the significance of the proposed controller for various road transportation challenges. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles)

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22 pages, 8092 KiB

Open AccessArticle

A Generic Image Processing Pipeline for Enhancing Accuracy and Robustness of Visual Odometry

by Mohamed Sabry, Mostafa Osman, Ahmed Hussein, Mohamed W. Mehrez, Soo Jeon and William Melek

Sensors 2022, 22(22), 8967; https://doi.org/10.3390/s22228967 - 19 Nov 2022

Cited by 3 | Viewed by 1555

Abstract

The accuracy of pose estimation from feature-based Visual Odometry (VO) algorithms is affected by several factors such as lighting conditions and outliers in the matched features. In this paper, a generic image processing pipeline is proposed to enhance the accuracy and robustness of feature-based VO algorithms. The pipeline consists of three stages, each addressing a problem that affects the performance of VO algorithms. The first stage tackles the lighting condition problem, where a filter called Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to the images to overcome changes in lighting in the environment. The second stage uses the Suppression via Square Covering (SSC) algorithm to ensure the features are distributed properly over the images. The last stage proposes a novel outliers rejection approach called the Angle-based Outlier Rejection (AOR) algorithm to remove the outliers generated in the feature matching process. The proposed pipeline is generic and modular and can be integrated with any type of feature-based VO (monocular, RGB-D, or stereo). The efficiency of the proposed pipeline is validated using sequences from KITTI (for stereo VO) and TUM (for RGB-D VO) datasets, as well as experimental sequences using an omnidirectional mobile robot (for monocular VO). The obtained results showed the performance gained by enhancing the accuracy and robustness of the VO algorithms without compromising on the computational cost using the proposed pipeline. The results are substantially better as opposed to not using the pipeline. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles)

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15 pages, 486 KiB

Open AccessArticle

Criticality Assessment Method for Automated Driving Systems by Introducing Fictive Vehicles and Variable Criticality Thresholds

by Demin Nalic, Tomislav Mihalj, Faris Orucevic, Martin Schabauer, Cornelia Lex, Wolfgang Sinz and Arno Eichberger

Sensors 2022, 22(22), 8780; https://doi.org/10.3390/s22228780 - 14 Nov 2022

Cited by 4 | Viewed by 1661

Abstract

The safety approval and assessment of automated driving systems (ADS) are becoming sophisticated and challenging tasks. Because the number of traffic scenarios is vast, it is essential to assess their criticality and extract the ones that present a safety risk. In this paper, we are proposing a novel method based on the time-to-react (TTR) measurement, which has advantages in considering avoidance possibilities. The method incorporates the concept of fictive vehicles and variable criticality thresholds (VCTs) to assess the overall scenario’s criticality. By introducing variable thresholds, a criticality scale is defined and used for criticality calculation. Based on this scale, the presented method determines the criticality of the lanes adjacent to the ego vehicle. This is performed by placing fictive vehicles in the adjacent lanes, which represent copies of the ego. The effectiveness of the method is demonstrated in two highway scenarios, with and without trailing vehicles. Results show different criticality for the two scenarios. The overall criticality of the scenario with trailing vehicles is higher due to the decrease in avoidance possibilities for the ego vehicle. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles)

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33 pages, 7645 KiB

Open AccessArticle

Local Path Planning of Autonomous Vehicle Based on an Improved Heuristic Bi-RRT Algorithm in Dynamic Obstacle Avoidance Environment

by Xiao Zhang, Tong Zhu, Lei Du, Yueqi Hu and Haoxue Liu

Sensors 2022, 22(20), 7968; https://doi.org/10.3390/s22207968 - 19 Oct 2022

Cited by 13 | Viewed by 2285

Abstract

The existing variants of the rapidly exploring random tree (RRT) cannot be effectively applied in local path planning of the autonomous vehicle and solve the coherence problem of paths between the front and back frames. Thus, an improved heuristic Bi-RRT algorithm is proposed, which is suitable for obstacle avoidance of the vehicle in an unknown dynamic environment. The vehicle constraint considering the driver’s driving habit and the obstacle-free direct connection mode of two random trees are introduced. Multi-sampling biased towards the target state reduces invalid searches, and parent node selection with the comprehensive measurement index accelerates the algorithm’s execution while making the initial path gentle. The adaptive greedy step size, introducing the target direction, expands the node more effectively. Moreover, path reorganization minimizes redundant path points and makes the path’s curvature continuous, and path coherence makes paths between the frames connect smoothly. Simulation analysis clarifies the efficient performance of the proposed algorithm, which can generate the smoothest path within the shortest time compared with the other four algorithms. Furthermore, the experiments on dynamic environments further show that the proposed algorithm can generate a differentiable coherence path, ensuring the ride comfort and stability of the vehicle. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles)

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19 pages, 3253 KiB

Open AccessArticle

Computational Efficient Motion Planning Method for Automated Vehicles Considering Dynamic Obstacle Avoidance and Traffic Interaction

by Yuxiang Zhang, Jiachen Wang, Jidong Lv, Bingzhao Gao, Hongqing Chu and Xiaoxiang Na

Sensors 2022, 22(19), 7397; https://doi.org/10.3390/s22197397 - 28 Sep 2022

Cited by 4 | Viewed by 1476

Abstract

In complex driving scenarios, automated vehicles should behave reasonably and respond adaptively with high computational efficiency. In this paper, a computational efficient motion planning method is proposed, which considers traffic interaction and accelerates calculation. Firstly, the behavior is decided by connecting the points on the unequally divided road segments and lane centerlines, which simplifies the decision-making process in both space and time span. Secondly, as the dynamic vehicle model with changeable longitudinal velocity is considered in the trajectory generation module, the C/GMRES algorithm is used to accelerate the calculation of trajectory generation and realize on-line solving in nonlinear model predictive control. Meanwhile, the motion of other traffic participants is more accurately predicted based on the driver’s intention and kinematics vehicle model, which enables the host vehicle to obtain a more reasonable behavior and trajectory. The simulation results verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles)

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22 pages, 3929 KiB

Open AccessArticle

Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving

by Fan Yang, Xueyuan Li, Qi Liu, Zirui Li and Xin Gao

Sensors 2022, 22(13), 4935; https://doi.org/10.3390/s22134935 - 29 Jun 2022

Cited by 8 | Viewed by 2249

Abstract

In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the interactive information between agents in the environment into the decision-making process, this paper proposes a generalized single-vehicle-based graph neural network reinforcement learning algorithm (SGRL algorithm). The SGRL algorithm introduces graph convolution into the traditional deep neural network (DQN) algorithm, adopts the training method for a single agent, designs a more explicit incentive reward function, and significantly improves the dimension of the action space. The SGRL algorithm is compared with the traditional DQN algorithm (NGRL) and the multi-agent training algorithm (MGRL) in the highway ramp scenario. Results show that the SGRL algorithm has outstanding advantages in network convergence, decision-making effect, and training efficiency. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles)

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24 pages, 8431 KiB

Open AccessArticle

Advanced Pedestrian State Sensing Method for Automated Patrol Vehicle Based on Multi-Sensor Fusion

by Pangwei Wang, Cheng Liu, Yunfeng Wang and Hongsheng Yu

Sensors 2022, 22(13), 4807; https://doi.org/10.3390/s22134807 - 25 Jun 2022

Cited by 3 | Viewed by 1760

Abstract

At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles)

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18 pages, 13610 KiB

Open AccessArticle

Multi-Agent Decision-Making Modes in Uncertain Interactive Traffic Scenarios via Graph Convolution-Based Deep Reinforcement Learning

by Xin Gao, Xueyuan Li, Qi Liu, Zirui Li, Fan Yang and Tian Luan

Sensors 2022, 22(12), 4586; https://doi.org/10.3390/s22124586 - 17 Jun 2022

Cited by 14 | Viewed by 2351

Abstract

As one of the main elements of reinforcement learning, the design of the reward function is often not given enough attention when reinforcement learning is used in concrete applications, which leads to unsatisfactory performances. In this study, a reward function matrix is proposed for training various decision-making modes with emphasis on decision-making styles and further emphasis on incentives and punishments. Additionally, we model a traffic scene via graph model to better represent the interaction between vehicles, and adopt the graph convolutional network (GCN) to extract the features of the graph structure to help the connected autonomous vehicles perform decision-making directly. Furthermore, we combine GCN with deep Q-learning and multi-step double deep Q-learning to train four decision-making modes, which are named the graph convolutional deep Q-network (GQN) and the multi-step double graph convolutional deep Q-network (MDGQN). In the simulation, the superiority of the reward function matrix is proved by comparing it with the baseline, and evaluation metrics are proposed to verify the performance differences among decision-making modes. Results show that the trained decision-making modes can satisfy various driving requirements, including task completion rate, safety requirements, comfort level, and completion efficiency, by adjusting the weight values in the reward function matrix. Finally, the decision-making modes trained by MDGQN had better performance in an uncertain highway exit scene than those trained by GQN. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles)

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22 pages, 13200 KiB

Open AccessArticle

Nonlinear Predictive Motion Control for Autonomous Mobile Robots Considering Active Fault-Tolerant Control and Regenerative Braking

by Peng Hang, Baichuan Lou and Chen Lv

Sensors 2022, 22(10), 3939; https://doi.org/10.3390/s22103939 - 23 May 2022

Cited by 7 | Viewed by 1945

Abstract

To further advance the performance and safety of autonomous mobile robots (AMRs), an integrated chassis control framework is proposed. In the longitudinal motion control module, a velocity-tracking controller was designed with the integrated feedforward and feedback control algorithm. Besides, the nonlinear model predictive control (NMPC) method was applied to the four-wheel steering (4WS) path-tracking controller design. To deal with the failure of key actuators, an active fault-tolerant control (AFTC) algorithm was designed by reallocating the driving or braking torques of the remaining normal actuators, and the weighted least squares (WLS) method was used for torque reallocation. The simulation results show that AMRs can advance driving stability and braking safety in the braking failure condition with the utilization of AFTC and recapture the braking energy during decelerations. Full article

(This article belongs to the Special Issue Advanced Sensing and Safety Control for Connected and Automated Vehicles)

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