A Real-time Path Planner based on Improved Tentacle Algorithm for Autonomous Vehicles

Zhuoren Li; Bo Leng; Chagen Luo; Liang Shao; Ruoling Yang; Chengen Tu; Arno Eichberger; Lu Xiong

doi:10.1109/ICBAIE56435.2022.9985832

A Real-time Path Planner based on Improved Tentacle Algorithm for Autonomous Vehicles

Zhuoren Li, Bo Leng^*, Chagen Luo, Liang Shao, Ruoling Yang, Chengen Tu, Arno Eichberger, Lu Xiong

^*Corresponding author for this work

Institute of Automotive Engineering (3310)

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

Abstract

In this paper, a real-time local path planner for obstacle avoidance of autonomous vehicles is proposed. Based on the tentacle algorithm, a novel tentacle curve that can ensure the continuity of the derivative of the path curvature is used to generate an alternate path set. After collision detection, we use the analytic hierarchy process (AHP) to calculate the weights to choose the best path. Finally, to verify the efficiency of the proposed planner, an obstacle avoidance case is designed in the simulation. The simulation results show that the proposed planner can obviously avoid the obstacle well and has better smoothness than the tentacle approach that uses the clothoid curve. The total time of local planning within a single period is less than 20 ms, which still has great real-time performance.

Original language	English
Title of host publication	2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022
Publisher	Institute of Electrical and Electronics Engineers
Pages	629-634
Number of pages	6
ISBN (Electronic)	9781665451604
DOIs	https://doi.org/10.1109/ICBAIE56435.2022.9985832
Publication status	Published - 2022
Event	3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering: ICBAIE 2022 - Virtual, Online, China Duration: 15 Jul 2022 → 17 Jul 2022

Conference

Conference	3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering
Abbreviated title	ICBAIE 2022
Country/Territory	China
City	Virtual, Online
Period	15/07/22 → 17/07/22

Keywords

autonomous vehicle
improved tentacle algorithm
obstacle avoidance
path planning

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Information Systems
Signal Processing
Information Systems and Management

Access to Document

10.1109/ICBAIE56435.2022.9985832

Cite this

Li, Z., Leng, B., Luo, C., Shao, L., Yang, R., Tu, C., Eichberger, A., & Xiong, L. (2022). A Real-time Path Planner based on Improved Tentacle Algorithm for Autonomous Vehicles. In 2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022 (pp. 629-634). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICBAIE56435.2022.9985832

A Real-time Path Planner based on Improved Tentacle Algorithm for Autonomous Vehicles. / Li, Zhuoren; Leng, Bo; Luo, Chagen et al.
2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022. Institute of Electrical and Electronics Engineers, 2022. p. 629-634.

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

Li, Z, Leng, B, Luo, C, Shao, L, Yang, R, Tu, C, Eichberger, A & Xiong, L 2022, A Real-time Path Planner based on Improved Tentacle Algorithm for Autonomous Vehicles. in 2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022. Institute of Electrical and Electronics Engineers, pp. 629-634, 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, Virtual, Online, China, 15/07/22. https://doi.org/10.1109/ICBAIE56435.2022.9985832

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title = "A Real-time Path Planner based on Improved Tentacle Algorithm for Autonomous Vehicles",

abstract = "In this paper, a real-time local path planner for obstacle avoidance of autonomous vehicles is proposed. Based on the tentacle algorithm, a novel tentacle curve that can ensure the continuity of the derivative of the path curvature is used to generate an alternate path set. After collision detection, we use the analytic hierarchy process (AHP) to calculate the weights to choose the best path. Finally, to verify the efficiency of the proposed planner, an obstacle avoidance case is designed in the simulation. The simulation results show that the proposed planner can obviously avoid the obstacle well and has better smoothness than the tentacle approach that uses the clothoid curve. The total time of local planning within a single period is less than 20 ms, which still has great real-time performance.",

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note = "Funding Information: ACKNOWLEDGMENT This work is supported by Shanghai Municipal Science and Technology Major Project 2021SHZDZX0100, the Fundamental Research Funds for the Central Universities and the Perspective Study Funding of Nanchang Automotive Institute of Intelligence and New Energy . Tongji University (TPD-TC202110-09). Publisher Copyright: {\textcopyright} 2022 IEEE.; 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering : ICBAIE 2022, ICBAIE 2022 ; Conference date: 15-07-2022 Through 17-07-2022",

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doi = "10.1109/ICBAIE56435.2022.9985832",

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AU - Tu, Chengen

AU - Eichberger, Arno

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N1 - Funding Information: ACKNOWLEDGMENT This work is supported by Shanghai Municipal Science and Technology Major Project 2021SHZDZX0100, the Fundamental Research Funds for the Central Universities and the Perspective Study Funding of Nanchang Automotive Institute of Intelligence and New Energy . Tongji University (TPD-TC202110-09). Publisher Copyright: © 2022 IEEE.

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N2 - In this paper, a real-time local path planner for obstacle avoidance of autonomous vehicles is proposed. Based on the tentacle algorithm, a novel tentacle curve that can ensure the continuity of the derivative of the path curvature is used to generate an alternate path set. After collision detection, we use the analytic hierarchy process (AHP) to calculate the weights to choose the best path. Finally, to verify the efficiency of the proposed planner, an obstacle avoidance case is designed in the simulation. The simulation results show that the proposed planner can obviously avoid the obstacle well and has better smoothness than the tentacle approach that uses the clothoid curve. The total time of local planning within a single period is less than 20 ms, which still has great real-time performance.

AB - In this paper, a real-time local path planner for obstacle avoidance of autonomous vehicles is proposed. Based on the tentacle algorithm, a novel tentacle curve that can ensure the continuity of the derivative of the path curvature is used to generate an alternate path set. After collision detection, we use the analytic hierarchy process (AHP) to calculate the weights to choose the best path. Finally, to verify the efficiency of the proposed planner, an obstacle avoidance case is designed in the simulation. The simulation results show that the proposed planner can obviously avoid the obstacle well and has better smoothness than the tentacle approach that uses the clothoid curve. The total time of local planning within a single period is less than 20 ms, which still has great real-time performance.

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A Real-time Path Planner based on Improved Tentacle Algorithm for Autonomous Vehicles

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

DVS: Vehicle Dynamics

Cite this

A Real-time Path Planner based on Improved Tentacle Algorithm for Autonomous Vehicles

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Projects

DVS: Vehicle Dynamics

Cite this