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Motion Planning and Control of Autonomous Vehicles
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Motion Planning and Control of Autonomous Vehicles

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2578

Special Issue Editor

Dr. Guoxing Bai

E-Mail Website
Guest Editor
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: path tracking of unmanned ground vehicles and mobile robots; model predictive control

Special Issue Information

Dear Colleagues,

Electric drive systems are easier to control than mechanical and hydraulic drive systems, so electric vehicles are more suitable as autonomous vehicles than as fuel vehicles. Therefore, autonomous vehicle technology can also be considered a derivative of electric vehicle technology. Motion planning and control limit the application of autonomous vehicles in faster and more complex environments. On the one hand, motion planning and control must consider electric motor constraints and vehicle dynamics constraints under faster speed conditions. On the other hand, in mines, harbours, or confined spaces, motion planning and control must consider the effects of complex and variable environmental constraints. Therefore, the topics of interest for this Special Issue include, but are not limited to:

  • The motion planning and control of electric vehicles that consider electric motor characteristics.
  • The motion planning and control of autonomous vehicles that considers vehicle dynamics.
  • The motion planning and control of autonomous vehicles considering emergency obstacle avoidance.
  • The motion planning and control of anti-roll-over processes.
  • The motion planning and control considering vehicle drive characteristics.
  • The motion planning and control that considers environmental uncertainties.
  • The motion planning and control for considering spatially constrained scenarios such as autonomous parking.
  • The motion planning and control considering energy constraints.
  • The motion planning and control in unique working environments such as mines.
  • The motion planning and control of special vehicles such as articulated steering vehicles.
  • Motion planning and control research, applying emerging algorithms, including, but not limited to, digital twins, reinforcement learning, etc.
  • Environmental parameter recognition for motion planning and control.

Generally, motion planning includes, but is not limited to, trajectory planning, path planning, and velocity planning. Motion control includes, but is not limited to, trajectory tracking, path tracking, velocity tracking, and point calibration control.

Dr. Guoxing Bai
Guest Editor

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. World Electric Vehicle Journal 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 1400 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

  • unmanned ground vehicles
  • motion planning
  • motion control
  • vehicle dynamics constraints
  • electric motor constraints
  • environmental uncertainties

Published Papers (3 papers)

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Research

14 pages, 2430 KiB  
Article
Deep Reinforcement Learning Lane-Changing Decision Algorithm for Intelligent Vehicles Combining LSTM Trajectory Prediction
by Zhengcai Yang, Zhengjun Wu, Yilin Wang and Haoran Wu
World Electr. Veh. J. 2024, 15(4), 173; https://doi.org/10.3390/wevj15040173 - 21 Apr 2024
Viewed by 454
Abstract
Intelligent decisions for autonomous lane-changing in vehicles have consistently been a focal point of research in the industry. Traditional lane-changing algorithms, which rely on predefined rules, are ill-suited for the complexities and variabilities of real-world road conditions. In this study, we propose an [...] Read more.
Intelligent decisions for autonomous lane-changing in vehicles have consistently been a focal point of research in the industry. Traditional lane-changing algorithms, which rely on predefined rules, are ill-suited for the complexities and variabilities of real-world road conditions. In this study, we propose an algorithm that leverages the deep deterministic policy gradient (DDPG) reinforcement learning, integrated with a long short-term memory (LSTM) trajectory prediction model, termed as LSTM-DDPG. In the proposed LSTM-DDPG model, the LSTM state module transforms the observed values from the observation module into a state representation, which then serves as a direct input to the DDPG actor network. Meanwhile, the LSTM prediction module translates the historical trajectory coordinates of nearby vehicles into a word-embedding vector via a fully connected layer, thus providing predicted trajectory information for surrounding vehicles. This integrated LSTM approach considers the potential influence of nearby vehicles on the lane-changing decisions of the subject vehicle. Furthermore, our study emphasizes the safety, efficiency, and comfort of the lane-changing process. Accordingly, we designed a reward and penalty function for the LSTM-DDPG algorithm and determined the optimal network structure parameters. The algorithm was then tested on a simulation platform built with MATLAB/Simulink. Our findings indicate that the LSTM-DDPG model offers a more realistic representation of traffic scenarios involving vehicle interactions. When compared to the traditional DDPG algorithm, the LSTM-DDPG achieved a 7.4% increase in average single-step rewards after normalization, underscoring its superior performance in enhancing lane-changing safety and efficiency. This research provides new ideas for advanced lane-changing decisions in autonomous vehicles. Full article
(This article belongs to the Special Issue Motion Planning and Control of Autonomous Vehicles)
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18 pages, 9953 KiB  
Article
Research on an Intelligent Vehicle Trajectory Tracking Method Based on Optimal Control Theory
by Shuang Wang, Gang Li, Jialin Song and Boju Liu
World Electr. Veh. J. 2024, 15(4), 160; https://doi.org/10.3390/wevj15040160 - 10 Apr 2024
Viewed by 741
Abstract
This study aims to explore an intelligent vehicle trajectory tracking control method based on optimal control theory. Considering the limitations of existing control strategies in dealing with signal delays and communication lags, a control strategy combining an anthropomorphic forward-looking reference path and longitudinal [...] Read more.
This study aims to explore an intelligent vehicle trajectory tracking control method based on optimal control theory. Considering the limitations of existing control strategies in dealing with signal delays and communication lags, a control strategy combining an anthropomorphic forward-looking reference path and longitudinal velocity closure is proposed to improve the accuracy and stability of intelligent vehicle trajectory tracking. Firstly, according to the vehicle dynamic error tracking model, a linear quadratic regulator (LQR) transverse controller is designed based on the optimal control principle, and a feedforward control strategy is added to reduce the system steady-state error. Secondly, an anthropomorphic look-ahead prediction model is established to mimic human driving behavior to compensate for the signal lag. The double proportional–integral–derivative (DPID) control algorithm is used to track the longitudinal speed reference value. Finally, a joint simulation is conducted based on MatLab/Simulink2021b and CarSim2019.0 software, and the effectiveness of the control strategy proposed in this paper is verified by constructing a semi-physical experimental platform and carrying out a hardware-in-the-loop test. The simulation and test results show that the control strategy can significantly improve the accuracy and stability of vehicle path tracking, which provides a new idea for future intelligent vehicle control system design. Full article
(This article belongs to the Special Issue Motion Planning and Control of Autonomous Vehicles)
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13 pages, 2042 KiB  
Article
Interactive Vehicle Trajectory Prediction for Highways Based on a Graph Attention Mechanism
by Zhenyu Song and Yubin Qian
World Electr. Veh. J. 2024, 15(3), 96; https://doi.org/10.3390/wevj15030096 - 5 Mar 2024
Viewed by 957
Abstract
Precise trajectory prediction is pivotal for autonomous vehicles operating in real-world traffic conditions, and can help them make the right decisions to ensure safety on the road. However, state-of-the-art approaches consider limited information about the historical movements of vehicles. On highways, drivers make [...] Read more.
Precise trajectory prediction is pivotal for autonomous vehicles operating in real-world traffic conditions, and can help them make the right decisions to ensure safety on the road. However, state-of-the-art approaches consider limited information about the historical movements of vehicles. On highways, drivers make their next judgments according to the behavior of the ambient vehicles. Thus, vehicles need to consider temporal and spatial interactions to reduce the risk of future collisions. In the current work, a trajectory prediction method is put forward in accordance with a graph attention mechanism. We add the absolute and relative motion information of vehicles to the input of the model to describe the vehicles’ past motion states more accurately. LSTM models are employed to process the historical motion information of vehicles, as well as the temporal correlations in interactions. The graph attention mechanism is applied to capture the spatial correlations between vehicles. Utilizing a decoder rooted in an LSTM framework, the future trajectory distribution is generated. Evaluation on the NGSIM US-101 and I-80 datasets substantiates the superiority of our approach over existing state-of-the-art algorithms. Moreover, the predictions of our model are analyzed. Full article
(This article belongs to the Special Issue Motion Planning and Control of Autonomous Vehicles)
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