Advances in Vehicle Dynamics Control and Motion Planning for Autonomous/Semi-autonomous Vehicles

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 13980

Special Issue Editors

Department of Mechanical Engineering, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Interests: decision-making; motion planning and control of connected and autonomous vehicles; unmanned vehicles and mobile robots
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Guest Editor
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi’an 710048, China
Interests: nonlinear robust control; adaptive control; vehicle dynamics control
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mechanical and Mechatronic Engineering, University of Technology Sydney, Broadway 2007, Australia
Interests: electrification; vehicle system dynamics; powertrains; vibration and control

Special Issue Information

Dear colleagues,

Traffic-related accidents are the eighth cause of death worldwide. Since the vast majority of accidents are caused by human error, autonomous vehicles could save hundreds of thousands of lives. Before that happens, however, many research challenges are yet to be solved.

An autonomous driving system mainly deals with three layers. First, the environment needs to be characterized with sufficient accuracy. Based on that, a motion planning algorithm decides the optimal path for the vehicle to follow. Given the desired trajectory, a vehicle dynamics controller ensures that the vehicle follows the given path while maintaining safety, comfort and, where possible, energy efficiency. 

In this framework, this Special Issue aims to propose a collection of studies with topics of interest including, but not limited to, the study and analysis of:

  • Advanced vehicle control for autonomous/semi-autonomous vehicles (AV-SAV);
  •  Control-oriented vehicle modeling;
  • Sensor fusion;
  • Motion planning/path following;
  • State estimation;
  • Electric and solar vehicles;
  • Machine learning algorithms and applications;
  • Advanced approaches in dealing with dynamics in AVs/SAVs;
  • Hybrid powertrain control;
  • Vibration control of vehicles.

Dr. Basilio Lenzo
Dr. Chuan Hu
Dr. Hui Pang
Dr. Paul Walker
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. Machines is an international peer-reviewed open access monthly 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 2400 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

  • vehicle dynamics
  • torque vectoring
  • yaw rate control
  • motion planning
  • autonomous driving
  • nonlinear model predictive control (NMPC)
  • optimization
  • localization
  • mapping
  • powertrain
  • sensors

Published Papers (5 papers)

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Research

15 pages, 5862 KiB  
Article
Test Evaluation Method for Lane Keeping Assistance System Using Dual Cameras
by Si-Ho Lee and Seon-Bong Lee
Machines 2021, 9(12), 310; https://doi.org/10.3390/machines9120310 - 25 Nov 2021
Cited by 1 | Viewed by 2213
Abstract
Recently, the number of vehicles equipped with the Lane Keeping Assistance System (LKAS) is increasing. Therefore, safety evaluation to validate the LKAS has become more important. However, the actual vehicle test for safety evaluation has disadvantages such as the need for professional manpower, [...] Read more.
Recently, the number of vehicles equipped with the Lane Keeping Assistance System (LKAS) is increasing. Therefore, safety evaluation to validate the LKAS has become more important. However, the actual vehicle test for safety evaluation has disadvantages such as the need for professional manpower, the use of expensive equipment, and environmental constraints. Therefore, we attempted to solve this problem using the dual cameras system with only inexpensive and accessible cameras. The optimal position of the dual cameras, image and focal length correction, and lane detection methods proposed in previous studies were used, and a theoretical equation for calculating the distance from the front wheel of the vehicle to the driving lane was proposed. For the actual vehicle testing, LKAS safety evaluation scenarios proposed in previous studies were used. According to the test results, the maximum error was 0.17 m, which indicated the reliability of the method because all errors in the tested scenarios exhibited similar trends and values. Therefore, through the use of the proposed theoretical equations in conjunction with inexpensive cameras, it is possible to reduce time, cost, and environmental problems in the development, vehicle application, and safety evaluation of LKAS components. Full article
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21 pages, 11493 KiB  
Article
Optimal Vibration Suppression Modification Method for High-Speed Helical Gear Transmission of Battery Electric Vehicles under Full Working Conditions
by Jinfu Du, Liang Hu, Jin Mao and Yanchao Zhang
Machines 2021, 9(10), 226; https://doi.org/10.3390/machines9100226 - 3 Oct 2021
Cited by 3 | Viewed by 2128
Abstract
To improve the working performance of battery electric vehicle (BEV) high-speed helical gear transmission under full working conditions, combined with Tooth Contact Analysis (TCA) and Loaded Tooth Contact Analysis (LTCA), the vibration model of single-stage helical gear bending-torsion-axis-swing coupling system considering time-varying mesh [...] Read more.
To improve the working performance of battery electric vehicle (BEV) high-speed helical gear transmission under full working conditions, combined with Tooth Contact Analysis (TCA) and Loaded Tooth Contact Analysis (LTCA), the vibration model of single-stage helical gear bending-torsion-axis-swing coupling system considering time-varying mesh stiffness was established. The genetic algorithm was used to optimize the tooth surface with the objective of minimizing the mean value of the vibration acceleration at full working conditions. Finally, a high-speed helical gear transmission system in a BEV gearbox was taken as a simulation example and the best-modified tooth surface at full working conditions was obtained. Experiment and simulation results show that the proposed calculation method of time-varying meshing stiffness is accurate, and tooth surface modification can effectively suppress the vibration of high-speed helical gear transmission in BEV; compared to the optimally modified tooth surface under a single load, the optimal modified tooth surface under full working conditions has a better vibration reduction effect over the entire working range. Full article
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16 pages, 4312 KiB  
Article
A Fuzzy Drive Strategy for an Intelligent Vehicle Controller Unit Integrated with Connected Data
by Luyao Du, Jun Ji, Donghua Zhang, Hongjiang Zheng and Wei Chen
Machines 2021, 9(10), 215; https://doi.org/10.3390/machines9100215 - 26 Sep 2021
Cited by 2 | Viewed by 1897
Abstract
In order to improve vehicle control safety in intelligent and connected environments, a fuzzy drive control strategy is proposed. Through the fusion of vehicle driving data, an early warning level model was established, and the fuzzy control method was used to obtain the [...] Read more.
In order to improve vehicle control safety in intelligent and connected environments, a fuzzy drive control strategy is proposed. Through the fusion of vehicle driving data, an early warning level model was established, and the fuzzy control method was used to obtain the appropriate torque command under the vehicle condition; torque optimization processing was performed according to the different corresponding vehicle following characteristics. The control strategy was tested and verified on an established platform. Based on the experimental results, compared with the traditional drive strategy in one-way front and rear following scenarios, the vehicle avoided excessive opening and closing of the accelerator pedal when the distance between vehicles was close, maintained the correct distance in the following situation, and had better dynamic response when the distance between vehicles was large, indicating that the proposed drive strategy had a better real-time and security performance. Full article
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15 pages, 3975 KiB  
Article
Research on Vehicle Adaptive Cruise Control Method Based on Fuzzy Model Predictive Control
by Jin Mao, Lei Yang, Yuanbo Hu, Kai Liu and Jinfu Du
Machines 2021, 9(8), 160; https://doi.org/10.3390/machines9080160 - 8 Aug 2021
Cited by 13 | Viewed by 3171
Abstract
Under complex working conditions, vehicle adaptive cruise control (ACC) systems with fixed weight coefficients cannot guarantee good car following performance under all conditions. In order to improve the tracking and comfort of vehicles in different modes, a fuzzy model predictive control (Fuzzy-MPC) algorithm [...] Read more.
Under complex working conditions, vehicle adaptive cruise control (ACC) systems with fixed weight coefficients cannot guarantee good car following performance under all conditions. In order to improve the tracking and comfort of vehicles in different modes, a fuzzy model predictive control (Fuzzy-MPC) algorithm is proposed. Based on the comprehensive consideration of safety, comfort, fuel economy and vehicle limitations, the objective function and constraints are designed. A relaxation factor vector is introduced to soften the hard constraint boundary in order to solve this problem, for which there was previously no feasible solution. In order to maintain driving stability under complex conditions, a multi-objective optimization method, which can update the weight coefficient online, is proposed. In the numerical simulation, the values of velocity, relative distance, acceleration and acceleration change rate under different conditions are compared, and the results show that the proposed algorithm has better tracking and stability than the traditional algorithm. The effectiveness and reliability of the Fuzzy-MPC algorithm are verified by co-simulation with the designed PID lower layer control algorithm with front feedforward and feedback. Full article
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15 pages, 5217 KiB  
Article
Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model
by Rongchao Jiang, Zhenchao Jin, Dawei Liu and Dengfeng Wang
Machines 2021, 9(6), 107; https://doi.org/10.3390/machines9060107 - 24 May 2021
Cited by 22 | Viewed by 2798
Abstract
In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. [...] Read more.
In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. Mesh morphing technology was employed to define design variables. Meanwhile, the rigid–flexible coupling vehicle model with flexible control arm and torsion beam was built for vehicle dynamic simulations. The total weight of control arm and torsion beam was taken as optimization objective, as well as ride comfort and handling stability performance indexes. In addition, the fatigue life, stiffness, and modal frequency of control arm and torsion beam were taken as the constraints. Then, Kriging model and NSGA-II were adopted to perform the multi-objective optimization of control arm and torsion beam for determining the lightweight scheme. By comparing the optimized and original design, it indicates that the weight of the optimized control arm and torsion beam are reduced 0.505 kg and 1.189 kg, respectively, while structural performance and vehicle performance satisfy the design requirement. The proposed multi-objective optimization method achieves a remarkable mass reduction, and proves to be feasible and effective for lightweight design of suspension components. Full article
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