A Review of Motion Planning Techniques for Automated Vehicles

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Open up Access Article

A Move Planning Method for Automated Vehicles in Dynamic Traffic Scenarios

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Schoolhouse of Transportation, Jilin Academy, Changchun 130022, China

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Key Laboratory of Route and Traffic Engineering in the Ministry building of Instruction, Tongji University, Shanghai 201804, China

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Author to whom correspondence should be addressed.

Bookish Editor: Jan Awrejcewicz

Received: 14 December 2021 / Revised: 17 Jan 2022 / Accepted: 18 Jan 2022 / Published: 21 Jan 2022

Abstract

We advise a motion planning method for automatic vehicles (AVs) to complete driving tasks in dynamic traffic scenes. The proposed method aims to generate motion trajectories for an AV after obtaining the surrounding dynamic data and making a preliminary driving determination. The method generates a reference line by interpolating the original waypoints and generates optional trajectories with costs in a prediction interval containing iii dimensions (lateral altitude, time, and velocity) in the Frenet frame, and filters the optimal trajectory by a series of threshold checks. When calculating the feasibility of optional trajectories, the cost of all optional trajectories after removing obstacle interference shows obvious axisymmetric regularity concerning the reference line. Based on this regularity, we apply the constrained Faux Annealing Algorithm (SAA) to better the process of searching for the optimal trajectories. Experiments in iii different simulated driving scenarios (speed maintaining, lane changing, and auto post-obit) show that the proposed method tin efficiently generate safe and comfortable motion trajectories for AVs in dynamic environments. Compared with the method of traversing sampling points in discrete infinite, the improved motion planning method saves 70.23% of the computation time, and overcomes the limitation of the spatial sampling interval. View Full-Text

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MDPI and ACS Style

Peng, B.; Yu, D.; Zhou, H.; Xiao, X.; Xie, C. A Motion Planning Method for Automatic Vehicles in Dynamic Traffic Scenarios. Symmetry 2022, fourteen, 208. https://doi.org/x.3390/sym14020208

AMA Style

Peng B, Yu D, Zhou H, Xiao X, Xie C. A Move Planning Method for Automated Vehicles in Dynamic Traffic Scenarios. Symmetry. 2022; xiv(2):208. https://doi.org/10.3390/sym14020208

Chicago/Turabian Style

Peng, Bo, Dexin Yu, Huxing Zhou, Xue Xiao, and Chen Xie. 2022. "A Motion Planning Method for Automated Vehicles in Dynamic Traffic Scenarios" Symmetry 14, no. 2: 208. https://doi.org/10.3390/sym14020208

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Author Biographies

BO PENG received the available'southward degree in measurement and control technology and instruments from Jilin University, Changchun, China, in 2017. He is currently pursuing the Ph.D. degree with the College of Transportation, Jilin Academy. His enquiry interest includes intelligent transportation systems.

DEXIN YU is a Professor at Transportation College of Jilin University. received the Ph.D. degrees in traffic Information Engineering and Control from Jilin University, Jilin, People's republic of china, in 2006. He has written more than ten manufactures and achieved seven patents in recent years. His research interests include Traffic Information Processing, Traffic System Analysis, etc. As the person in charge of the project, he has presided over and completed more than 20 national, provincial and ministerial and engineering industrialization projects in China, including 6 national high-tech enquiry plans (863 programme) and 1 national natural fund project. As the main finisher, he won 2 second prizes for national scientific and technological progress, and three provincial and ministerial science and engineering science awards.

HUXING ZHOU received the Ph.D. degree in traffic information engineering and control from Jilin Academy, Jilin, Communist china, in 2013. He is currently a Lecturer with the School of Transportation, Jilin University. He has written more than viii articles and received three patents in recent years. His research interests include intelligent transportation systems and the Internet of Vehicles. He has presided over one basic scientific research project of the Jilin Instruction Department Scientific discipline and Technology Project, and participated in more 10 projects, including the National 863 Program, the National Natural Science Foundation, and the Scientific discipline and Engineering Evolution Project of Jilin Province.

XUE XIAO received the bachelor's degree in traffic transportation from Shenyang Jianzhu University, Shenyang, People's republic of china, in 2017. She received the master's degree in traffic data engineering and control from Jilin Academy, Changchun, China, in 2020. She is currently pursuing the Ph.D. degree with the College of Transportation, Tongji University. Her research interest includes intelligent transportation systems.

CHEN XIE received the bachelor'south caste in traffic engineering science from Jilin Academy, Changchun, People's republic of china, in 2019. She is currently pursuing the Ph.D. degree with the Higher of Transportation, Jilin University. Her inquiry interest includes intelligent transportation systems.

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Source: https://www.mdpi.com/2073-8994/14/2/208

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