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

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Fahrzeugtechnik (3310)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

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.

Originalsprache	englisch
Titel	2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022
Herausgeber (Verlag)	Institute of Electrical and Electronics Engineers
Seiten	629-634
Seitenumfang	6
ISBN (elektronisch)	9781665451604
DOIs	https://doi.org/10.1109/ICBAIE56435.2022.9985832
Publikationsstatus	Veröffentlicht - 2022
Veranstaltung	3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering: ICBAIE 2022 - Virtual, Online, China Dauer: 15 Juli 2022 → 17 Juli 2022

Konferenz

Konferenz	3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering
Kurztitel	ICBAIE 2022
Land/Gebiet	China
Ort	Virtual, Online
Zeitraum	15/07/22 → 17/07/22

ASJC Scopus subject areas

Artificial intelligence
Computernetzwerke und -kommunikation
Information systems
Signalverarbeitung
Informationssysteme und -management

Zugriff auf Dokument

10.1109/ICBAIE56435.2022.9985832

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

DVS: Vehicle Dynamics
Koglbauer, I. V., Lex, C., Shao, L., Semmer, M., Rogic, B., Peer, M., Hackl, A., Sternat, A. S., Schabauer, M., Samiee, S., Eichberger, A., Ager, M., Malić, D., Wohlfahrter, H., Scherndl, C., Magosi, Z. F., Orucevic, F., Puščul, D., Arefnezhad, S., Karoshi, P., Schöttel, C. E., Pandurevic, A., Harcevic, A., Wellershaus, C., Li, H., Mihalj, T., Kanuric, T., Gu, Z., Wallner, D., De Cristofaro, F., Soboleva, K., Nalic, D., Bernsteiner, S., Kraus, H., Zhao, Y., Bodner, J., Bui, D. T., Hirschberg, W., Plöckinger, M. & Khoshnood Sarabi, N.
1/01/11 → 31/12/24
Projekt: Arbeitsgebiet

Dieses zitieren

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 (S. 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. S. 629-634.

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

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, S. 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

AU - Xiong, Lu

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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Y1 - 2022

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

Konferenz

ASJC Scopus subject areas

Zugriff auf Dokument

Andere Dateien und Links

Fingerprint

Projekte

DVS: Vehicle Dynamics

Dieses zitieren