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Advanced Sensing and 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 (15 May 2021) | Viewed by 44581

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Special Issue Editors

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
Special Issues, Collections and Topics in MDPI journals

Dr. Haiping Du

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Guest Editor

Faulty of Engineering and Information Science, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: vehicle dynamics and control systems; robust control theory and engineering applications; robotics and automation
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Dr. Wanzhong Zhao

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Guest Editor

Nanjing University of Aeronautics & Astronautics, China
Interests: vehicle control and intelligence

Prof. Dr. Yifan Zhao

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Guest Editor

School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, UK
Interests: computing, simulation and modelling; human factors; industrial automation; instrumentation, sensors and measurement science; systems engineering; through-life engineering services
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Dr. Fuwu Yan

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Guest Editor

Wuhan University of Technology, China
Interests: electrification of vehicles; powertrain control; autonomous vehicles

Dr. Chen Lyu

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Guest Editor

Associate Professor, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: automated driving; human–machine systems; intelligent electric vehicles; human–robot collaboration; cyber–physical systems
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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;
Fault diagnosis and fault-tolerant control of CAVs;
Sensing and control for multimodal vehicles (e.g., ground, aerial, underwater).

Dr. Chao Huang
Dr. Haiping Du
Dr. Wanzhong Zhao
Dr. Yifan Zhao
Dr. Fuwu Yan
Dr. Chen Lv
Guest Editors

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

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Published Papers (13 papers)

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Editorial

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4 pages, 168 KiB

Open AccessEditorial

Advanced Sensing and Control for Connected and Automated Vehicles

by Chao Huang, Haiping Du, Wanzhong Zhao, Yifan Zhao, Fuwu Yan and Chen Lv

Sensors 2022, 22(4), 1538; https://doi.org/10.3390/s22041538 - 16 Feb 2022

Cited by 2 | Viewed by 2109

Abstract

In recent years, connected and automated vehicles (CAV) have been a transformative technology that is expected to reduce emissions and change and improve the safety and efficiency of the mobilities [...] Full article

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

Research

Jump to: Editorial

28 pages, 7808 KiB

Open AccessArticle

A Three-Dimensional Integrated Non-Linear Coordinate Control Framework for Combined Yaw- and Roll-Stability Control during Tyre Blow-Out

by Boyuan Li, Chao Huang, Yang Wu, Bangji Zhang and Haiping Du

Sensors 2021, 21(24), 8328; https://doi.org/10.3390/s21248328 - 13 Dec 2021

Cited by 3 | Viewed by 2325

Abstract

A tyre blow-out can greatly affect vehicle stability and cause serious accidents. In the literature, however, studies on comprehensive three-dimensional vehicle dynamics modelling and stability control strategies in the event of a sudden tyre blow-out are seriously lacking. In this study, a comprehensive 14 degrees-of-freedom (DOF) vehicle dynamics model is first proposed to describe the vehicle yaw-plane and roll-plane dynamics performance after a tyre blow-out. Then, based on the proposed 14 DOF dynamics model, an integrated control framework for a combined yaw plane and roll-plane stability control is presented. This integrated control framework consists of a vehicle state predictor, an upper-level control mode supervisor and a lower-level 14 DOF model predictive controller (MPC). The state predictor is designed to predict the vehicle’s future states, and the upper-level control mode supervisor can use these future states to determine a suitable control mode. After that, based on the selected control mode, the lower-level MPC can control the individual driving actuator to achieve the combined yaw plane and roll plane control. Finally, a series of simulation tests are conducted to verify the effectiveness of the proposed control strategy. Full article

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

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17 pages, 52292 KiB

Open AccessArticle

A New Trajectory Tracking Algorithm for Autonomous Vehicles Based on Model Predictive Control

by Zhejun Huang, Huiyun Li, Wenfei Li, Jia Liu, Chao Huang, Zhiheng Yang and Wenqi Fang

Sensors 2021, 21(21), 7165; https://doi.org/10.3390/s21217165 - 28 Oct 2021

Cited by 15 | Viewed by 3601

Abstract

Trajectory tracking is a key technology for precisely controlling autonomous vehicles. In this paper, we propose a trajectory-tracking method based on model predictive control. Instead of using the forward Euler integration method, the backward Euler integration method is used to establish the predictive model. To meet the real-time requirement, a constraint is imposed on the control law and the warm-start technique is employed. The MPC-based controller is proved to be stable. The simulation results demonstrate that, at the cost of no or a little increase in computational time, the tracking performance of the controller is much better than that of controllers using the forward Euler method. The maximum lateral errors are reduced by 69.09%, 47.89% and 78.66%. The real-time performance of the MPC controller is good. The calculation time is below 0.0203 s, which is shorter than the control period. Full article

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

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23 pages, 6228 KiB

Open AccessArticle

Unified Chassis Control of Electric Vehicles Considering Wheel Vertical Vibrations

by Xinbo Chen, Mingyang Wang and Wei Wang

Sensors 2021, 21(11), 3931; https://doi.org/10.3390/s21113931 - 7 Jun 2021

Cited by 7 | Viewed by 3253

Abstract

In the process of vehicle chassis electrification, different active actuators and systems have been developed and commercialized for improved vehicle dynamic performances. For a vehicle system with actuation redundancy, the integration of individual chassis control systems can provide additional benefits compared to a single ABS/ESC system. This paper describes a Unified Chassis Control (UCC) strategy for enhancing vehicle stability and ride comfort by the coordination of four In-Wheel Drive (IWD), 4-Wheel Independent Steering (4WIS), and Active Suspension Systems (ASS). Desired chassis motion is determined by generalized forces/moment calculated through a high-level sliding mode controller. Based on tire force constraints subject to allocated normal forces, the generalized forces/moment are distributed to the slip and slip angle of each tire by a fixed-point control allocation algorithm. Regarding the uneven road, H∞ robust controllers are proposed based on a modified quarter-car model. Evaluation of the overall system was accomplished by simulation testing with a full-vehicle CarSim model under different scenarios. The conclusion shows that the vertical vibration of the four wheels plays a detrimental role in vehicle stability, and the proposed method can effectively realize the tire force distribution to control the vehicle body attitude and driving stability even in high-demanding scenarios. Full article

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

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17 pages, 3628 KiB

Open AccessArticle

Estimation of Vehicle Dynamic Parameters Based on the Two-Stage Estimation Method

by Wenfei Li, Huiyun Li, Kun Xu, Zhejun Huang, Ke Li and Haiping Du

Sensors 2021, 21(11), 3711; https://doi.org/10.3390/s21113711 - 26 May 2021

Cited by 7 | Viewed by 3496

Abstract

Vehicle dynamic parameters are of vital importance to establish feasible vehicle models which are used to provide active controls and automated driving control. However, most vehicle dynamics parameters are difficult to obtain directly. In this paper, a new method, which requires only conventional sensors, is proposed to estimate vehicle dynamic parameters. The influence of vehicle dynamic parameters on vehicle dynamics often involves coupling. To solve the problem of coupling, a two-stage estimation method, consisting of multiple-models and the Unscented Kalman Filter, is proposed in this paper. During the first stage, the longitudinal vehicle dynamics model is used. Through vehicle acceleration/deceleration, this model can be used to estimate the distance between the vehicle centroid and vehicle front, the height of vehicle centroid and tire longitudinal stiffness. The estimated parameter can be used in the second stage. During the second stage, a single-track with roll dynamics vehicle model is adopted. By making vehicle continuous steering, this vehicle model can be used to estimate tire cornering stiffness, the vehicle moment of inertia around the yaw axis and the moment of inertia around the longitudinal axis. The simulation results show that the proposed method is effective and vehicle dynamic parameters can be well estimated. Full article

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

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24 pages, 9593 KiB

Open AccessArticle

A Novel V2V Cooperative Collision Warning System Using UWB/DR for Intelligent Vehicles

by Mingyang Wang, Xinbo Chen, Baobao Jin, Pengyuan Lv, Wei Wang and Yong Shen

Sensors 2021, 21(10), 3485; https://doi.org/10.3390/s21103485 - 17 May 2021

Cited by 11 | Viewed by 2997

Abstract

The collision warning system (CWS) plays an essential role in vehicle active safety. However, traditional distance-measuring solutions, e.g., millimeter-wave radars, ultrasonic radars, and lidars, fail to reflect vehicles’ relative attitude and motion trends. In this paper, we proposed a vehicle-to-vehicle (V2V) cooperative collision warning system (CCWS) consisting of an ultra-wideband (UWB) relative positioning/directing module and a dead reckoning (DR) module with wheel-speed sensors. Each vehicle has four UWB modules on the body corners and two wheel-speed sensors on the rear wheels in the presented configuration. An over-constrained localization method is proposed to calculate the relative position and orientation with the UWB data more accurately. Vehicle velocities and yaw rates are measured by wheel-speed sensors. An extended Kalman filter (EKF) is applied based on the relative kinematic model to combine the UWB and DR data. Finally, the time to collision (TTC) is estimated based on the predicted vehicle collision position. Furthermore, through UWB signals, vehicles can simultaneously communicate with each other and share information, e.g., velocity, yaw rate, which brings the potential for enhanced real-time performance. Simulation and experimental results show that the proposed method significantly improves the positioning, directing, and velocity estimating accuracy, and the proposed system can efficiently provide collision warning. Full article

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

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16 pages, 4938 KiB

Open AccessArticle

Small Object Detection in Traffic Scenes Based on Attention Feature Fusion

by Jing Lian, Yuhang Yin, Linhui Li, Zhenghao Wang and Yafu Zhou

Sensors 2021, 21(9), 3031; https://doi.org/10.3390/s21093031 - 26 Apr 2021

Cited by 40 | Viewed by 4975

Abstract

There are many small objects in traffic scenes, but due to their low resolution and limited information, their detection is still a challenge. Small object detection is very important for the understanding of traffic scene environments. To improve the detection accuracy of small objects in traffic scenes, we propose a small object detection method in traffic scenes based on attention feature fusion. First, a multi-scale channel attention block (MS-CAB) is designed, which uses local and global scales to aggregate the effective information of the feature maps. Based on this block, an attention feature fusion block (AFFB) is proposed, which can better integrate contextual information from different layers. Finally, the AFFB is used to replace the linear fusion module in the object detection network and obtain the final network structure. The experimental results show that, compared to the benchmark model YOLOv5s, this method has achieved a higher mean Average Precison (mAP) under the premise of ensuring real-time performance. It increases the mAP of all objects by 0.9 percentage points on the validation set of the traffic scene dataset BDD100K, and at the same time, increases the mAP of small objects by 3.5%. Full article

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

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36 pages, 4469 KiB

Open AccessArticle

Distributed Urban Platooning towards High Flexibility, Adaptability, and Stability

by Sangsoo Jeong, Youngmi Baek and Sang H. Son

Sensors 2021, 21(8), 2684; https://doi.org/10.3390/s21082684 - 10 Apr 2021

Cited by 7 | Viewed by 2384

Abstract

Vehicle platooning reduces the safety distance between vehicles and the travel time of vehicles so that it leads to an increase in road capacity and to saving fuel consumption. In Europe, many projects for vehicle platooning are being actively developed, but mostly focus on truck platooning on the highway with a simpler topology than that of the urban road. When an existing vehicle platoon is applied to urban roads, many challenges are more complicated to address than highways. They include complex topology, various routes, traffic signals, intersections, frequent lane change, and communication interference depending on a higher vehicle density. To address these challenges, we propose a distributed urban platooning protocol (DUPP) that enables high mobility and maximizes flexibility for driving vehicles to conduct urban platooning in a decentralized manner. DUPP has simple procedures to perform platooning maneuvers and does not require explicit conforming for the completion of platooning maneuvers. Since DUPP mainly operates on a service channel, it does not cause negative side effects on the exchange of basic safety messages on a control channel. Moreover, DUPP does not generate any data propagation delay due to contention-based channel access since it guarantees sequential data transmission opportunities for urban platooning vehicles. Finally, to address a problem of the broadcast storm while vehicles notify detected road events, DUPP performs forwarder selection using an analytic hierarchy process. The performance of the proposed DUPP is compared with that of ENSEMBLE which is the latest European platooning project in terms of the travel time of vehicles, the lifetime of an urban platoon, the success ratio of a designed maneuver, the external cost and the periodicity of the urban platooning-related transmissions, the adaptability of an urban platoon, and the forwarder selection ratio for each vehicle. The results of the performance evaluation demonstrate that the proposed DUPP is well suited to dynamic urban environments by maintaining a vehicle platoon as stable as possible after DUPP flexibly and quickly forms a vehicle platoon without the support of a centralized node. Full article

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

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17 pages, 4986 KiB

Open AccessArticle

Data-Driven Object Vehicle Estimation by Radar Accuracy Modeling with Weighted Interpolation

by Woo Young Choi, Jin Ho Yang and Chung Choo Chung

Sensors 2021, 21(7), 2317; https://doi.org/10.3390/s21072317 - 26 Mar 2021

Cited by 7 | Viewed by 2771

Abstract

For accurate object vehicle estimation using radar, there are two fundamental problems: measurement uncertainties in calculating an object’s position with a virtual polygon box and latency due to commercial radar tracking algorithms. We present a data-driven object vehicle estimation scheme to solve measurement uncertainty and latency problems in radar systems. A radar accuracy model and latency coordination are proposed to reduce the tracking error. We first design data-driven radar accuracy models to improve the accuracy of estimation determined by the object vehicle’s position. The proposed model solves the measurement uncertainty problem within a feasible set for error covariance. The latency coordination is developed by analyzing the position error according to the relative velocity. The position error by latency is stored in a feasible set for relative velocity, and the solution is calculated from the given relative velocity. Removing the measurement uncertainty and latency of the radar system allows for a weighted interpolation to be applied to estimate the position of the object vehicle. Our method is tested by a scenario-based estimation experiment to validate the usefulness of the proposed data-driven object vehicle estimation scheme. We confirm that the proposed estimation method produces improved performance over the conventional radar estimation and previous methods. Full article

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

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19 pages, 8734 KiB

Open AccessArticle

An Evaluation of Executive Control Function and Its Relationship with Driving Performance

by Lirong Yan, Tiantian Wen, Jiawen Zhang, Le Chang, Yi Wang, Mutian Liu, Changhao Ding and Fuwu Yan

Sensors 2021, 21(5), 1763; https://doi.org/10.3390/s21051763 - 4 Mar 2021

Cited by 6 | Viewed by 1985

Abstract

The driver’s attentional state is a significant human factor in traffic safety. The executive control process is a crucial sub-function of attention. To explore the relationship between the driver’s driving performance and executive control function, a total of 35 healthy subjects were invited to take part in a simulated driving experiment and a task-cuing experiment. The subjects were divided into three groups according to their driving performance (aberrant driving behaviors, including lapses and errors) by the clustering method. Then the performance efficiency and electroencephalogram (EEG) data acquired in the task-cuing experiment were compared among the three groups. The effect of group, task transition types and cue-stimulus intervals (CSIs) were statistically analyzed by using the repeated measures analysis of variance (ANOVA) and the post hoc simple effect analysis. The subjects with lower driving error rates had better executive control efficiency as indicated by the reaction time (RT) and error rate in the task-cuing experiment, which was related with their better capability to allocate the available attentional resources, to express the external stimuli and to process the information in the nervous system, especially the fronto-parietal network. The activation degree of the frontal area fluctuated, and of the parietal area gradually increased along with the increase of CSI, which implied the role of the frontal area in task setting reconstruction and working memory maintaining, and of the parietal area in stimulus–Response (S–R) mapping expression. This research presented evidence of the close relationship between executive control functions and driving performance. Full article

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

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20 pages, 1227 KiB

Open AccessArticle

Multi-Task End-to-End Self-Driving Architecture for CAV Platoons

by Sebastian Huch, Aybike Ongel, Johannes Betz and Markus Lienkamp

Sensors 2021, 21(4), 1039; https://doi.org/10.3390/s21041039 - 3 Feb 2021

Cited by 10 | Viewed by 2990

Abstract

Connected and autonomous vehicles (CAVs) could reduce emissions, increase road safety, and enhance ride comfort. Multiple CAVs can form a CAV platoon with a close inter-vehicle distance, which can further improve energy efficiency, save space, and reduce travel time. To date, there have been few detailed studies of self-driving algorithms for CAV platoons in urban areas. In this paper, we therefore propose a self-driving architecture combining the sensing, planning, and control for CAV platoons in an end-to-end fashion. Our multi-task model can switch between two tasks to drive either the leading or following vehicle in the platoon. The architecture is based on an end-to-end deep learning approach and predicts the control commands, i.e., steering and throttle/brake, with a single neural network. The inputs for this network are images from a front-facing camera, enhanced by information transmitted via vehicle-to-vehicle (V2V) communication. The model is trained with data captured in a simulated urban environment with dynamic traffic. We compare our approach with different concepts used in the state-of-the-art end-to-end self-driving research, such as the implementation of recurrent neural networks or transfer learning. Experiments in the simulation were conducted to test the model in different urban environments. A CAV platoon consisting of two vehicles, each controlled by an instance of the network, completed on average 67% of the predefined point-to-point routes in the training environment and 40% in a never-seen-before environment. Using V2V communication, our approach eliminates casual confusion for the following vehicle, which is a known limitation of end-to-end self-driving. Full article

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

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22 pages, 4431 KiB

Open AccessArticle

Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving

by Bing Lu, Hongwen He, Huilong Yu, Hong Wang, Guofa Li, Man Shi and Dongpu Cao

Sensors 2020, 20(24), 7197; https://doi.org/10.3390/s20247197 - 16 Dec 2020

Cited by 22 | Viewed by 3276

Abstract

The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving. Full article

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

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26 pages, 12679 KiB

Open AccessArticle

A Novel Path Planning Algorithm for Truck Platooning Using V2V Communication

by Yongki Lee, Taewon Ahn, Chanhwa Lee, Sangjun Kim and Kihong Park

Sensors 2020, 20(24), 7022; https://doi.org/10.3390/s20247022 - 8 Dec 2020

Cited by 17 | Viewed by 6435

Abstract

In truck platooning, the leading vehicle is driven manually, and the following vehicles run by autonomous driving, with the short inter-vehicle distance between trucks. To successfully perform platooning in various situations, each truck must maintain dynamic stability, and furthermore, the whole system must maintain string stability. Due to the short front-view range, however, the following vehicles’ path planning capabilities become significantly impaired. In addition, in platooning with articulated cargo trucks, the off-tracking phenomenon occurring on a curved road makes it hard for the following vehicle to track the trajectory of the preceding truck. In addition, without knowledge of the global coordinate system, it is difficult to correlate the local coordinate systems that each truck relies on for sensing environment and dynamic signals. In this paper, in order to solve these problems, a path planning algorithm for platooning of articulated cargo trucks has been developed. Using the Kalman filter, V2V (Vehicle-to-Vehicle) communication, and a novel update-and-conversion method, each following vehicle can accurately compute the trajectory of the leading vehicle’s front part for using it as a target path. The path planning algorithm of this paper was validated by simulations on severe driving scenarios and by tests on an actual road. The results demonstrated that the algorithm could provide lateral string stability and robustness for truck platooning. Full article

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

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