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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

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.

Originalspracheenglisch
Titel2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
Seiten629-634
Seitenumfang6
ISBN (elektronisch)9781665451604
DOIs
PublikationsstatusVeröffentlicht - 2022
Veranstaltung3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering: ICBAIE 2022 - Virtual, Online, China
Dauer: 15 Juli 202217 Juli 2022

Konferenz

Konferenz3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering
KurztitelICBAIE 2022
Land/GebietChina
OrtVirtual, Online
Zeitraum15/07/2217/07/22

ASJC Scopus subject areas

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

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