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Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (13 September 2024) | Viewed by 17661

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Special Issue Editors

Dr. Ying Zhang

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Guest Editor

School of Computer Science, Northwestern Polytechnical University, Xi’an 710060, China
Interests: system cognition; human–machine cooperation; swarm intelligence; intelligent decision-making; energy-saving control with application to electric; connected and autonomous vehicles (e-CAVs); unmanned aerial vehicles (UAVs)
Special Issues, Collections and Topics in MDPI journals

Dr. Hao Chen

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Guest Editor

Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
Interests: electric machines; power electronic drives; motor control

Dr. Jinchao Chen

E-Mail Website
Guest Editor

School of Computer Science, Northwestern Polytechnical University, Xi’an 710060, China
Interests: simulation; verification; decision-making and intelligent control of unmanned aerial vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, with the ever-accelerated development and progressive maturation of significant technologies, autonomous vehicles (e.g., self-driving cars, unmanned aerial vehicles, etc.) have moved from the stage of laboratories to open road field tests and even commercial demonstrations in urban driving scenarios and specific traffic environments. Autonomous vehicles play an increasingly important role in the national economy and human social life in many fields. Compared with human-driven vehicles, autonomous vehicles possess many advanced functions that do not require driver involvement, such as lane changing/keeping, adaptive cruise control, decision-making, and motion control. Due to the dynamic and uncertain nature of driving scenarios, specific traffic environments impose many theoretical and technical challenges on existing autonomous vehicle techniques. For these reasons, how autonomous vehicles can be programmed to behave in different driving situations to guarantee safety and efficiency remain among the crucial knowledge gaps that require scientific research.

This Special Issue calls for papers presenting novel works regarding scene understanding, trajectory prediction, decision-making, motion planning and the intelligent control of autonomous vehicles. Manuscripts considered for possible publication may focus on, but are not limited to, the following areas:

The environmental perception of autonomous vehicles in specific driving scenarios;
The modeling of autonomous vehicles in specific driving scenarios;
Decision-making for the improved safety of autonomous vehicles;
Multi-agent reinforcement learning for autonomous vehicles;
The trajectory prediction of autonomous vehicles in specific driving scenarios;
Localization, mapping and the connection of autonomous vehicles;
The motion planning of autonomous vehicles;
Eco-driving in autonomous vehicles;
Human–machine collaborative control of autonomous vehicles;
Vehicle motion control in complex traffic environments;
Vehicle-to-infrastructure cooperation.

Dr. Ying Zhang
Dr. Hao Chen
Dr. Jinchao Chen
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. Electronics 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 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

autonomous vehicles
trajectory prediction
motion planning
intelligent driving
vehicle motion control

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Related Special Issue

Advances in Autonomous Vehicles: Motion Planning, Trajectory Prediction and Control, 2nd Edition in Electronics

Published Papers (10 papers)

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Research

19 pages, 4234 KiB

Open AccessArticle

Channel Estimation Algorithm Based on Parrot Optimizer in 5G Communication Systems

by Ke Sun and Jiwei Xu

Electronics 2024, 13(17), 3522; https://doi.org/10.3390/electronics13173522 - 5 Sep 2024

Cited by 2 | Viewed by 1583

Abstract

Accurate and efficient channel estimation (CE) is critical in the context of autonomous driving. This paper addresses the issue of orthogonal frequency-division multiplexing (OFDM) channel estimation in 5G communication systems by proposing a channel estimation model based on the Parrot Optimizer (PO). The model optimizes for the minimum bit error rate (BER) and the minimum mean square error (MMSE) using the Parrot Optimizer to estimate the optimal channel characteristics. Simulation experiments compared the performance of PO-CE with the Least Squares (LS) method and the MMSE method under various signal-to-noise ratios (SNR) and modulation schemes. The results demonstrate that PO-CE’s performance approximates that of MMSE under high SNR conditions and significantly outperforms LS in the absence of prior information. The experiments specifically included scenarios with different modulation schemes (QPSK, 16QAM, 64QAM, and 256QAM) and pilot densities (1/3, 1/6, 1/9, and 1/12). The findings indicate that PO-CE has substantial potential for application in 5G channel estimation, offering an effective method for optimizing wireless communication systems. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)

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17 pages, 1888 KiB

Open AccessArticle

Adaptive Aberrance Repressed Correlation Filters with Cooperative Optimization in High-Dimensional Unmanned Aerial Vehicle Task Allocation and Trajectory Planning

by Zijie Zheng, Zhijun Zhang, Zhenzhang Li, Qiuda Yu and Ya Jiang

Electronics 2024, 13(15), 3071; https://doi.org/10.3390/electronics13153071 - 2 Aug 2024

Viewed by 961

Abstract

In the rapidly evolving field of unmanned aerial vehicle (UAV) applications, the complexity of task planning and trajectory optimization, particularly in high-dimensional operational environments, is increasingly challenging. This study addresses these challenges by developing the Adaptive Distortion Suppression Correlation Filter Cooperative Optimization (ARCF-ICO) algorithm, designed for high-dimensional UAV task allocation and trajectory planning. The ARCF-ICO algorithm combines advanced correlation filter technologies with multi-objective optimization techniques, enhancing the precision of trajectory planning and efficiency of task allocation. By incorporating weather conditions and other environmental factors, the algorithm ensures robust performance at low altitudes. The ARCF-ICO algorithm improves UAV tracking stability and accuracy by suppressing distortions, facilitating optimal path selection and task execution. Experimental validation using the UAV123@10fps and OTB-100 datasets demonstrates that the ARCF-ICO algorithm outperforms existing methods in Area Under the Curve (AUC) and Precision metrics. Additionally, the algorithm’s consideration of battery consumption and endurance further validates its applicability to current UAV technologies. This research advances UAV mission planning and sets new standards for UAV deployment in both civilian and military applications, where adaptability and accuracy are critical. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)

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20 pages, 5437 KiB

Open AccessArticle

The Trajectory Prediction of Spacecraft under the Influence of Gyroscopic Effect Generated during Non-Keplerian Motion

by Xuehua Li, Lei Zhang and Zhijun Li

Electronics 2024, 13(14), 2793; https://doi.org/10.3390/electronics13142793 - 16 Jul 2024

Viewed by 1101

Abstract

Due to perturbation forces and control forces, trajectories of spacecraft around the Earth are usually non-Keplerian orbits, which may result in a gyroscopic effect. To meet the complex demands of space operations in the future, the trajectory prediction of spacecraft under the influence of the gyroscopic effect generated during non-Keplerian motion needs to be studied in depth. The paper investigated the trajectory of spacecraft under the gyroscopic effect generated during non-Keplerian motion. Firstly, according to the similarity between the spacecraft precession motion and the gyroscopic precession, as well as the definition of the “gyroscopic effect” of high-speed rotating bodies, the “gyroscopic effect” generated during the non-Keplerian motion of spacecraft around the earth was defined. Then, taking a continuous radial thrust orbit as an example, the dynamics equations of spacecraft under the influence of gyroscopic effect were deduced. Through theoretical analysis and numerical simulation, the trajectory of spacecraft under the influence of the gyroscopic effect generated during non-Keplerian motion was investigated. Finally, the paper simulated the examples and tested the performance of the proposed method. Simulation results show that a large gyroscopic moment may be generated in some non-Keplerian motion of the spacecraft. The greater the rotational angular velocity of the orbital plane, the greater the gyroscopic moment. Due to the gyroscopic effect, there is a significant deviation in the orbit and the orbital elements compared to those without considering the gyroscopic effect, which indicates that the influence of the gyroscopic effect generated during non-Keplerian motion on the orbit of the spacecraft cannot be ignored. It can be seen from the simulation results that the gyroscopic effect has a significant influence on the trajectory of spacecraft. In some special cases, the gyroscopic effect can be utilized reasonably to save fuel and realize low-energy orbit maneuver control technology in actual space missions; but the control should be considered for the spacecraft to bring it back to the desired orbit in most cases. It is necessary to study the trajectory of spacecraft under the influence of gyroscopic effect. The method and conclusions proposed can provide a theoretical reference for spacecraft trajectory prediction and future large-scale fast orbital maneuvers to meet the needs of complex space operations. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)

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17 pages, 3734 KiB

Open AccessArticle

E-DNet: An End-to-End Dual-Branch Network for Driver Steering Intention Detection

by Youjia Fu, Huixia Xue, Junsong Fu and Zihao Xu

Electronics 2024, 13(13), 2477; https://doi.org/10.3390/electronics13132477 - 25 Jun 2024

Viewed by 1227

Abstract

An advanced driving assistant system (ADAS) is critical for improving traffic efficiency and ensuring driving safety. By anticipating the driver’s steering intentions in advance, the system can alert the driver in time to avoid a vehicle collision. This paper proposes a novel end-to-end dual-branch network (EDNet) that utilizes both in-cabin and out-of-cabin data. In this study, we designed an in-cabin driver intent feature extractor based on 3D residual networks and atrous convolution, which is applicable to video data and is capable of capturing a larger range of driver behavior. In order to capture the long-term dependency of temporal data, we designed the depthwise-separable max-pooling (DSMax) module and combined it with a convolutional LSTM to obtain the road environment feature extractor outside the cabin. In addition, to effectively fuse different features inside and outside the cockpit, we designed and propose the dynamic combined-feature attention fusion (D-CAF) module. EDNet employs a freeze-training method, which enables the creation of a lightweight model while simultaneously enhancing the final classification accuracy. Extensive experiments on the Brain4Cars dataset and the Zenodo dataset show that the proposed EDNet was able to recognize the driver’s steering intention up to 3 s in advance. It outperformed the existing state of the art in most driving scenarios. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)

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17 pages, 6993 KiB

Open AccessArticle

An Improved YOLOv5s Model for Building Detection

by Jingyi Zhao, Yifan Li, Jing Cao, Yutai Gu, Yuanze Wu, Chong Chen and Yingying Wang

Electronics 2024, 13(11), 2197; https://doi.org/10.3390/electronics13112197 - 4 Jun 2024

Viewed by 2262

Abstract

With the continuous advancement of autonomous vehicle technology, the recognition of buildings becomes increasingly crucial. It enables autonomous vehicles to better comprehend their surrounding environment, facilitating safer navigation and decision-making processes. Therefore, it is significant to improve detection efficiency on edge devices. However, building recognition faces problems such as severe occlusion and large size of detection models that cannot be deployed on edge devices. To solve these problems, a lightweight building recognition model based on YOLOv5s is proposed in this study. We first collected a building dataset from real scenes and the internet, and applied an improved GridMask data augmentation method to expand the dataset and reduce the impact of occlusion. To make the model lightweight, we pruned the model by the channel pruning method, which decreases the computational costs of the model. Furthermore, we used Mish as the activation function to help the model converge better in sparse training. Finally, comparing it to YOLOv5s (baseline), the experiments show that the improved model reduces the model size by 9.595 MB, and the mAP@0.5 reaches 82.3%. This study will offer insights into lightweight building detection, demonstrating its significance in environmental perception, monitoring, and detection, particularly in the field of autonomous driving. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)

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16 pages, 849 KiB

Open AccessArticle

Self-Evaluation of Trajectory Predictors for Autonomous Driving

by Phillip Karle, Lukas Furtner and Markus Lienkamp

Electronics 2024, 13(5), 946; https://doi.org/10.3390/electronics13050946 - 29 Feb 2024

Cited by 3 | Viewed by 1702

Abstract

Driving experience and anticipatory driving are essential skills for humans to operate vehicles in complex environments. In the context of autonomous vehicles, the software must offer the related features of scenario understanding and motion prediction. The latter feature of motion prediction is extensively researched with several competing large datasets, and established methods provide promising results. However, the incorporation of scenario understanding has been sparsely investigated. It comprises two aspects. First, by means of scenario understanding, individual assumptions of an object’s behavior can be derived to adaptively predict its future motion. Second, scenario understanding enables the detection of challenging scenarios for autonomous vehicle software to prevent safety-critical situations. Therefore, we propose a method incorporating scenario understanding into the motion prediction task to improve adaptivity and avoid prediction failures. This is realized by an a priori evaluation of the scenario based on semantic information. The evaluation adaptively selects the most accurate prediction model but also recognizes if no model is capable of accurately predicting this scenario and high prediction errors are expected. The results on the comprehensive scenario library CommonRoad reveal a decrease in the Euclidean prediction error by

81.0

% and a

90.8

% reduction in mispredictions of our method compared to the benchmark model. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)

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29 pages, 5823 KiB

Open AccessArticle

A Personalized Motion Planning Method with Driver Characteristics in Longitudinal and Lateral Directions

by Di Zeng, Ling Zheng, Yinong Li, Jie Zeng and Kan Wang

Electronics 2023, 12(24), 5021; https://doi.org/10.3390/electronics12245021 - 15 Dec 2023

Cited by 2 | Viewed by 1465

Abstract

Humanlike driving is significant in improving the safety and comfort of automated vehicles. This paper proposes a personalized motion planning method with driver characteristics in longitudinal and lateral directions for highway automated driving. The motion planning is decoupled into path optimization and speed optimization under the framework of the Baidu Apollo EM motion planner. For modeling driver behavior in the longitudinal direction, a car-following model is developed and integrated into the speed optimizer based on a weight ratio hypothesis model of the objective functional, whose parameters are obtained by Bayesian optimization and leave-one-out cross validation using the driving data. For modeling driver behavior in the lateral direction, a Bayesian network (BN), which maps the physical states of the ego vehicle and surrounding vehicles and the lateral intentions of the surrounding vehicles to the driver’s lateral intentions, is built in an efficient and lightweight way using driving data. Further, a personalized reference trajectory decider is developed based on the BN, considering traffic regulations, the driver’s preference, and the costs of the trajectories. According to the actual traffic scenarios in the driving data, a simulation is constructed, and the results validate the human likeness of the proposed motion planning method. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)

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27 pages, 36598 KiB

Open AccessArticle

SoC-VRP: A Deep-Reinforcement-Learning-Based Vehicle Route Planning Mechanism for Service-Oriented Cooperative ITS

by Boyuan Hou, Kailong Zhang, Zu Gong, Qiugang Li, Junle Zhou, Jiahao Zhang and Arnaud de La Fortelle

Electronics 2023, 12(20), 4191; https://doi.org/10.3390/electronics12204191 - 10 Oct 2023

Cited by 7 | Viewed by 2831

Abstract

With the rapid development of emerging information technology and its increasing integration with transportation systems, the Intelligent Transportation System (ITS) is entering a new phase, called Cooperative ITS (C-ITS). It offers promising solutions to numerous challenges in traditional transportation systems, among which the Vehicle Routing Problem (VRP) is a significant concern addressed in this work. Considering the varying urgency levels of different vehicles and their different traveling constraints in the Service-oriented Cooperative ITS (SoC-ITS) framework studied in our previous research, the Service-oriented Cooperative Vehicle Routing Problem (SoC-VRP) is firstly analyzed, in which cooperative planning and vehicle urgency degrees are two vital factors. After examining the characteristics of both VRP and SoC-VRP, a Deep Reinforcement Learning (DRL)-based prioritized route planning mechanism is proposed. Specifically, we establish a deep reinforcement learning model with Rainbow DQN and devise a prioritized successive decision-making route planning method for SoC-ITS, where vehicle urgency degrees are mapped to three priorities: High for emergency vehicles, Medium for shuttle buses, and Low for the rest. All proposed models and methods are implemented, trained using various scenarios on typical road networks, and verified with SUMO-based scenes. Experimental results demonstrate the effectiveness of this hybrid prioritized route planning mechanism. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)

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21 pages, 5262 KiB

Open AccessArticle

Research on Multiple Air-To-Air Refueling Planning Based on Multi-Dimensional Improved NSGA-II Algorithm

by Zhihao Zhang, Zhouhang Huang, Xiaodong Liu and Boyu Feng

Electronics 2023, 12(18), 3880; https://doi.org/10.3390/electronics12183880 - 14 Sep 2023

Cited by 3 | Viewed by 1746

Abstract

Reasonable air-to-air refueling planning (AARP) is essential for the successful completion of remote flight missions. A comprehensive task model for air refueling planning is proposed, and the key constraints are determined. The multi-objective optimization algorithm NSGA-II is improved from three distinct perspectives. The performance of the improved NSGA-II was evaluated by selecting test functions from the ZDT series for comparison against the original version. Simulation experiments demonstrate that the improved NSGA-II yields an increase in the average hypervolume index by approximately 10% to 18%, a decrease in the average spacing index by about 22% to 57%, and a reduction in the standard deviation of hypervolume by 27% to 76%. The obtained findings demonstrate that the improved NSGA-II variant exhibits superior convergence, uniformity, and universality. The airspace of the Americas was selected as the mission area to generate 50 AARP schemes for application. Five representative schemes with fuel consumption from 47,083 kg to 104,735 kg, corresponding to time consumption coefficient from 1.27 to 1.07, were chosen as alternatives. This research can enhance the efficiency and stability of air-to-air refueling planning, thereby serving as a valuable theoretical reference for selecting appropriate remote multi-point air refueling schemes. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)

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19 pages, 3595 KiB

Open AccessArticle

Application of the Relative Orbit in an On-Orbit Service Mission

by Xuehua Li, Lei Zhang, Zhijun Li and Xingsuo He

Electronics 2023, 12(14), 3034; https://doi.org/10.3390/electronics12143034 - 11 Jul 2023

Cited by 2 | Viewed by 1195

Abstract

To achieve an on-orbit service mission, the mission spacecraft must approach the target spacecraft first, which is based on the spacecraft’s relative motion. To enhance the safety and reliability of on-orbit service missions, the relative hovering orbit was proposed and needed to be studied further. A high-precision design method for hovering orbit is presented based on the relative dynamics model of spacecraft in this paper. Firstly, based on the stability analysis of the spacecraft relative dynamics model, a method to determine the initial value of periodic relative motion orbit is explored, and an example is given to verify the validity of the method. Then, through theoretical analysis, the formulae of control acceleration required during the hovering flying mission were put forward for both without considering perturbation and with considering J₂ perturbation, and numerical simulations for hovering orbit were made to verify the feasibility of the approaches proposed. Simulation results show that the control acceleration curves are smooth, which indicates that the hovering flying mission is easier to achieve, and the control method based on sliding mode control theory is adopted for hovering control. The relative hovering method proposed can provide references in space on-orbit service missions for practical engineers. Full article

(This article belongs to the Special Issue Advances in Autonomous Vehicle: Motion Planning, Trajectory Prediction and Control)

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