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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: closed (30 September 2024) | Viewed by 6714

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

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Published Papers (6 papers)

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Research

21 pages, 7260 KiB  
Article
Path Tracking for Electric Mining Articulated Vehicles Based on Nonlinear Compensated Multiple Reference Points Linear MPC
by Guoxing Bai, Shaochong Liu, Bining Zhou, Jianxiu Huang, Yan Zheng and Elxat Elham
World Electr. Veh. J. 2024, 15(9), 427; https://doi.org/10.3390/wevj15090427 - 20 Sep 2024
Viewed by 361
Abstract
The path tracking control of electric mining articulated vehicles (EMAVs), critical equipment commonly used for mining and transportation in underground mines, is a research topic that has received much attention. The path tracking control of EMAVs is subject to several system constraints, including [...] Read more.
The path tracking control of electric mining articulated vehicles (EMAVs), critical equipment commonly used for mining and transportation in underground mines, is a research topic that has received much attention. The path tracking control of EMAVs is subject to several system constraints, including articulation angle and articulation angular velocity. In light of this, many researchers have initiated studies based on model predictive control (MPC). The principal design schemes for existing MPC methods encompass linear MPC (LMPC) utilizing a single reference point, so named the single reference point LMPC (SRP-LMPC), and nonlinear MPC (NMPC). However, NMPC exhibits suboptimal real-time performance, while SRP-LMPC demonstrates inferior accuracy. To simultaneously improve the accuracy and real-time performance of the path tracking control of EMAV, based on the SRP-LMPC, a path tracking control method for EMAV based on nonlinear compensated multiple reference points LMPC (MRP-LMPC) is proposed. The simulation results demonstrate that MRP-LMPC simultaneously exhibits a commendable degree of accuracy and real-time performance. In all simulation results, the displacement error amplitude and heading error amplitude of MRP-LMPC do not exceed 0.2675 m and 0.1108 rad, respectively. Additionally, the maximum solution time in each control period is 5.9580 ms. The accuracy of MRP-LMPC is comparable to that of NMPC. However, the maximum solution time of MRP-LMPC can be reduced by over 27.81% relative to that of NMPC. Furthermore, the accuracy of MRP-LMPC is significantly superior to that of SRP-LMPC. The maximum displacement and heading error amplitude can be reduced by 0.3075 m and 0.1003 rad, respectively, representing a reduction of 65.51% and 73.59% in the middle speed and above scenario. Full article
(This article belongs to the Special Issue Motion Planning and Control of Autonomous Vehicles)
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14 pages, 9376 KiB  
Article
Research on Motion Control and Compensation of UAV Shipborne Autonomous Landing Platform
by Xin Liu, Mingzhi Shao, Tengwen Zhang, Hansheng Zhou, Lei Song, Fengguang Jia, Chengmeng Sun and Zhuoyi Yang
World Electr. Veh. J. 2024, 15(9), 388; https://doi.org/10.3390/wevj15090388 - 27 Aug 2024
Viewed by 629
Abstract
As an important interface between unmanned aerial vehicles (UAVs) and ships, the stability and motion control compensation technology of the shipborne UAV landing platform are paramount for successful UAV landings. This paper has designed a new control compensation method for an autonomous UAV [...] Read more.
As an important interface between unmanned aerial vehicles (UAVs) and ships, the stability and motion control compensation technology of the shipborne UAV landing platform are paramount for successful UAV landings. This paper has designed a new control compensation method for an autonomous UAV landing platform to address the impact of complex sea conditions on the stability of UAV landing platforms. Firstly, the parallel Stewart platform was introduced as the landing platform, and its structure was analyzed with forward and inverse kinematic calculations conducted in Matlab to verify its accuracy. Secondly, a least-squares recursive AR prediction algorithm was designed to predict the future attitudes of ships under varying sea conditions. Finally, the prediction algorithm was combined with the platform’s control strategy and a dual-sensor system was adopted to ensure the stability of the UAV landing process. The experimental results demonstrate that these innovative improvements enhanced the compensation accuracy by 59.6%, 60.3%, 48.4%, and 47.9% for the rolling angles of 5° and 10° and the pitching angles of 5° and 10°, respectively. Additionally, the compensation accuracy for the roll and pitch in sea states 2 and 5 improved by 51.2%, 59.4%, 58.7%, and 55.9%, respectively, providing technical support for UAV missions such as maritime rescue and exploration. Full article
(This article belongs to the Special Issue Motion Planning and Control of Autonomous Vehicles)
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22 pages, 7744 KiB  
Article
Improved Taillight Detection Model for Intelligent Vehicle Lane-Change Decision-Making Based on YOLOv8
by Ming Li, Jian Zhang, Weixia Li, Tianrui Yin, Wei Chen, Luyao Du, Xingzhuo Yan and Huiheng Liu
World Electr. Veh. J. 2024, 15(8), 369; https://doi.org/10.3390/wevj15080369 - 15 Aug 2024
Cited by 1 | Viewed by 690
Abstract
With the rapid advancement of autonomous driving technology, the recognition of vehicle lane-changing can provide effective environmental parameters for vehicle motion planning, decision-making and control, and has become a key task for intelligent vehicles. In this paper, an improved method for vehicle taillight [...] Read more.
With the rapid advancement of autonomous driving technology, the recognition of vehicle lane-changing can provide effective environmental parameters for vehicle motion planning, decision-making and control, and has become a key task for intelligent vehicles. In this paper, an improved method for vehicle taillight detection and intent recognition based on YOLOv8 (You Only Look Once version 8) is proposed. Firstly, the CARAFE (Context-Aware Reassembly Operator) module is introduced to address fine perception issues of small targets, enhancing taillight detection accuracy. Secondly, the TriAtt (Triplet Attention Mechanism) module is employed to improve the model’s focus on key features, particularly in the identification of positive samples, thereby increasing model robustness. Finally, by optimizing the EfficientP2Head (a small object auxiliary head based on depth-wise separable convolutions) module, the detection capability for small targets is further strengthened while maintaining the model’s practicality and lightweight characteristics. Upon evaluation, the enhanced algorithm demonstrates impressive results, achieving a precision rate of 93.27%, a recall rate of 79.86%, and a mean average precision (mAP) of 85.48%, which shows that the proposed method could effectively achieve taillight detection. Full article
(This article belongs to the Special Issue Motion Planning and Control of Autonomous Vehicles)
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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
Cited by 1 | Viewed by 1324
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 1453
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 1369
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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