Real-Time Motion Planning and Control for a Formula Student Driverless Car

Tairan Chen; Xinyu Gao; Chenrui Huang; Xiang Li; Shaokun Yang; Hailong Gong; Yunji Feng

doi:10.1007/978-981-16-2090-4_12

Real-Time Motion Planning and Control for a Formula Student Driverless Car

Tairan Chen^*, Xinyu Gao, Chenrui Huang, Xiang Li, Shaokun Yang, Hailong Gong, Yunji Feng

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper presents a planning and control pipeline for an autonomous race car to drive around a track that may not be previously known for three laps. In the case of a limited perception range in the first lap, boundary detection and motion planning separately extract and optimize the trajectory to minimize the trajectory curvature. After finishing the mapping, multi-strategy NMPC is used to optimize or track the trajectory. We use the real-world map data from Formula Student Autonomous China 2019 for experiments. The experiment shows that under the same vehicle model, the system can significantly improve the performance of the race car.

Original language	English
Title of host publication	Proceedings of China SAE Congress 2020
Subtitle of host publication	Selected Papers
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	203-219
Number of pages	17
ISBN (Print)	9789811620898
DOIs	https://doi.org/10.1007/978-981-16-2090-4_12
Publication status	Published - 2022
Event	China SAE Congress, 2020 - Shanghai, China Duration: 27 Oct 2020 → 29 Oct 2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	769
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	China SAE Congress, 2020
Country/Territory	China
City	Shanghai
Period	27/10/20 → 29/10/20

Keywords

Boundary detection
Formula student autonomous China
Model predictive control
Motion planning

Access to Document

10.1007/978-981-16-2090-4_12

Cite this

Chen, T., Gao, X., Huang, C., Li, X., Yang, S., Gong, H., & Feng, Y. (2022). Real-Time Motion Planning and Control for a Formula Student Driverless Car. In Proceedings of China SAE Congress 2020: Selected Papers (pp. 203-219). (Lecture Notes in Electrical Engineering; Vol. 769). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-2090-4_12

@inproceedings{041bfe209d45492bb77fcc2c8c219b61,

title = "Real-Time Motion Planning and Control for a Formula Student Driverless Car",

abstract = "This paper presents a planning and control pipeline for an autonomous race car to drive around a track that may not be previously known for three laps. In the case of a limited perception range in the first lap, boundary detection and motion planning separately extract and optimize the trajectory to minimize the trajectory curvature. After finishing the mapping, multi-strategy NMPC is used to optimize or track the trajectory. We use the real-world map data from Formula Student Autonomous China 2019 for experiments. The experiment shows that under the same vehicle model, the system can significantly improve the performance of the race car.",

keywords = "Boundary detection, Formula student autonomous China, Model predictive control, Motion planning",

author = "Tairan Chen and Xinyu Gao and Chenrui Huang and Xiang Li and Shaokun Yang and Hailong Gong and Yunji Feng",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; China SAE Congress, 2020 ; Conference date: 27-10-2020 Through 29-10-2020",

year = "2022",

doi = "10.1007/978-981-16-2090-4_12",

language = "English",

isbn = "9789811620898",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

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booktitle = "Proceedings of China SAE Congress 2020",

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}

Chen, T, Gao, X, Huang, C, Li, X, Yang, S, Gong, H & Feng, Y 2022, Real-Time Motion Planning and Control for a Formula Student Driverless Car. in Proceedings of China SAE Congress 2020: Selected Papers. Lecture Notes in Electrical Engineering, vol. 769, Springer Science and Business Media Deutschland GmbH, pp. 203-219, China SAE Congress, 2020, Shanghai, China, 27/10/20. https://doi.org/10.1007/978-981-16-2090-4_12

Real-Time Motion Planning and Control for a Formula Student Driverless Car. / Chen, Tairan; Gao, Xinyu; Huang, Chenrui et al.
Proceedings of China SAE Congress 2020: Selected Papers. Springer Science and Business Media Deutschland GmbH, 2022. p. 203-219 (Lecture Notes in Electrical Engineering; Vol. 769).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Gong, Hailong

AU - Feng, Yunji

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N2 - This paper presents a planning and control pipeline for an autonomous race car to drive around a track that may not be previously known for three laps. In the case of a limited perception range in the first lap, boundary detection and motion planning separately extract and optimize the trajectory to minimize the trajectory curvature. After finishing the mapping, multi-strategy NMPC is used to optimize or track the trajectory. We use the real-world map data from Formula Student Autonomous China 2019 for experiments. The experiment shows that under the same vehicle model, the system can significantly improve the performance of the race car.

AB - This paper presents a planning and control pipeline for an autonomous race car to drive around a track that may not be previously known for three laps. In the case of a limited perception range in the first lap, boundary detection and motion planning separately extract and optimize the trajectory to minimize the trajectory curvature. After finishing the mapping, multi-strategy NMPC is used to optimize or track the trajectory. We use the real-world map data from Formula Student Autonomous China 2019 for experiments. The experiment shows that under the same vehicle model, the system can significantly improve the performance of the race car.

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Real-Time Motion Planning and Control for a Formula Student Driverless Car

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