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Sensor Fusion and Advanced Controller for Connected and Automated Vehicles...
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Sensor Fusion and Advanced Controller for Connected and Automated Vehicles (Volume II)

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1420

Special Issue Editors

Dr. Boyuan Li

E-Mail Website
Guest Editor
Zhejiang Lab, Kechuang Avenue, Hangzhou 311121, China
Interests: state estimation; vehicle dynamics and control; path planning and path tracking control
Special Issues, Collections and Topics in MDPI journals
Dr. Yafei Wang

E-Mail Website
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. Georgios Papaioannou

E-Mail Website
Guest Editor
Intelligent Vehicles & Cognitive Robotics, Technische Universiteit Delft, Mekelweg 5, 2628 CD Delft, The Netherlands
Interests: motion comfort; chassis design and optimisation; vehicle dynamics and control; tyre dynamics and tyre wear
Special Issues, Collections and Topics in MDPI journals
Dr. Haiping Du

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For conventional on-road vehicles, due to a lack of adequate sensor information, vehicle dynamics controllers can only rely on the dedicated state estimators, such as the sideslip angle estimator and the velocity estimator. Sometimes the estimation results are not reliable due to the single estimator source. However, autonomous electric vehicles are equipped with a number of advanced sensors such as radar and cameras. The measurements of these additional sensors can be fused into the vehicle state estimators to build a sensor fusion system, which can lead to a large number of highly reliable estimated vehicle states. This enriched vehicle state information can be greatly beneficial to the complex integrated advanced controller design (such as path planning, a path-tracking controller, or integrated chassis control) for automated vehicles or automated vehicles in a connected vehicle platoon.

We welcome the submission of both review articles and original research papers relating the sensor fusion strategy design or vehicle dynamics controller design for connected and automated vehicles. There is a particular interest in papers focusing on how advanced controllers for autonomous vehicles can fully utilize the states estimated from sensor fusion systems to maximise the control performance of automated passenger vehicles or heavy vehicles.

Dr. Boyuan Li
Dr. Yafei Wang
Dr. Georgios Papaioannou
Dr. Haiping Du
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All 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.

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • state estimator
  • sensor fusion
  • automated vehicles
  • connected vehicles
  • integrated controller
  • path planning control
  • path tracking control

Related Special Issue

Published Papers (3 papers)

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Research

14 pages, 1687 KiB  
Article
Research on Tire Surface Damage Detection Method Based on Image Processing
by Jiaqi Chen, Aijuan Li, Fei Zheng, Shanshan Chen, Weikai He and Guangping Zhang
Sensors 2024, 24(9), 2778; https://doi.org/10.3390/s24092778 - 26 Apr 2024
Viewed by 152
Abstract
The performance of the tire has a very important impact on the safe driving of the car, and in the actual use of the tire, due to complex road conditions or use conditions, it will inevitably cause immeasurable wear, scratches and other damage. [...] Read more.
The performance of the tire has a very important impact on the safe driving of the car, and in the actual use of the tire, due to complex road conditions or use conditions, it will inevitably cause immeasurable wear, scratches and other damage. In order to effectively detect the damage existing in the key parts of the tire, a tire surface damage detection method based on image processing was proposed. In this method, the image of tire side is captured by camera first. Then, the collected images are preprocessed by optimizing the multi-scale bilateral filtering algorithm to enhance the detailed information of the damaged area, and the optimization effect is obvious. Thirdly, the image segmentation based on clustering algorithm is carried out. Finally, the Harris corner detection method is used to capture the “salt and pepper” corner of the target region, and the segmsegmed binary image is screened and matched based on histogram correlation, and the target region is finally obtained. The experimental results show that the similarity detection is accurate, and the damage area can meet the requirements of accurate identification. Full article
(This article belongs to the Special Issue Sensor Fusion and Advanced Controller for Connected and Automated Vehicles (Volume II))
16 pages, 5409 KiB  
Article
Global Dynamic Path Planning of AGV Based on Fusion of Improved A* Algorithm and Dynamic Window Method
by Te Wang, Aijuan Li, Dongjin Guo, Guangkai Du and Weikai He
Sensors 2024, 24(6), 2011; https://doi.org/10.3390/s24062011 - 21 Mar 2024
Viewed by 507
Abstract
Designed to meet the demands of AGV global optimal path planning and dynamic obstacle avoidance, this paper proposes a combination of an improved A* algorithm and dynamic window method fusion algorithm. Firstly, the heuristic function is dynamically weighted to reduce the search scope [...] Read more.
Designed to meet the demands of AGV global optimal path planning and dynamic obstacle avoidance, this paper proposes a combination of an improved A* algorithm and dynamic window method fusion algorithm. Firstly, the heuristic function is dynamically weighted to reduce the search scope and improve the planning efficiency; secondly, a path-optimization method is introduced to eliminate redundant nodes and redundant turning points in the path; thirdly, combined with the improved A* algorithm and dynamic window method, the local dynamic obstacle avoidance in the global optimal path is realized. Finally, the effectiveness of the proposed method is verified by simulation experiments. According to the results of simulation analysis, the path-planning time of the improved A* algorithm is 26.3% shorter than the traditional A* algorithm, the search scope is 57.9% less, the path length is 7.2% shorter, the number of path nodes is 85.7% less, and the number of turning points is 71.4% less. The fusion algorithm can evade moving obstacles and unknown static obstacles in different map environments in real time along the global optimal path. Full article
(This article belongs to the Special Issue Sensor Fusion and Advanced Controller for Connected and Automated Vehicles (Volume II))
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19 pages, 1647 KiB  
Article
Predictive Path-Tracking Control of an Autonomous Electric Vehicle with Various Multi-Actuation Topologies
by Chenhui Lin, Boyuan Li, Efstathios Siampis, Stefano Longo and Efstathios Velenis
Sensors 2024, 24(5), 1566; https://doi.org/10.3390/s24051566 - 28 Feb 2024
Viewed by 432
Abstract
This paper presents the development of path-tracking control strategies for an over-actuated autonomous electric vehicle. The vehicle platform is equipped with four-wheel steering (4WS) as well as torque vectoring (TV) capabilities, which enable the control of vehicle dynamics to be enhanced. A nonlinear [...] Read more.
This paper presents the development of path-tracking control strategies for an over-actuated autonomous electric vehicle. The vehicle platform is equipped with four-wheel steering (4WS) as well as torque vectoring (TV) capabilities, which enable the control of vehicle dynamics to be enhanced. A nonlinear model predictive controller is proposed taking into account the nonlinearities in vehicle dynamics at the limits of handling as well as the crucial actuator constraints. Controllers with different actuation formulations are presented and compared to study the path-tracking performance of the vehicle with different levels of actuation. The controllers are implemented in a high-fidelity simulation environment considering scenarios of vehicle handling limits. According to the simulation results, the vehicle achieves the best overall path-tracking performance with combined 4WS and TV, which illustrates that the over-actuation topology can enhance the path-tracking performance during conditions under the limits of handling. In addition, the performance of the over-actuation controller is further assessed with different sampling times as well as prediction horizons in order to investigate the effect of such parameters on the control performance, and its capability for real-time execution. In the end, the over-actuation control strategy is implemented on a target machine for real-time validation. The control formulation proposed in this paper is proven to be compatible with different levels of actuation, and it is also demonstrated in this work that it is possible to include the particular over-actuation formulation and specific nonlinear vehicle dynamics in real-time operation, with the sampling time and prediction time providing a compromise between path-tracking performance and computational time. Full article
(This article belongs to the Special Issue Sensor Fusion and Advanced Controller for Connected and Automated Vehicles (Volume II))
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