Adaptive Potential Field-Based Path Planning for Complex Autonomous Driving Scenarios

Bing Lu, Guofa Li, Huilong Yu*, Hong Wang, Jinquan Guo, Dongpu Cao, Hongwen He*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)

Abstract

An adaptive potential field is designed to adapt the acceleration/deceleration and mass of the obstacle. The potential fields are established in a transformed road coordinate system to improve the feasibility and robustness. A path planning method is proposed based on the designed adaptive potential field to improve the driving safety and the ride comfort of autonomous vehicles in complex driving scenarios, which including the cut-in, emergency braking, obstacle sudden accelerating during overtaking and the curve road driving scenarios. The effectiveness of the proposed method is validated by simulations with constructed and real data, respectively. The $TTC\text{s}$ (Time-to-Collision) and the maximum lateral accelerations are used to evaluate the improvements on safety and ride comfort. The results show that both the driving safety and ride comfort are efficiently improved by using the proposed approach in emergency braking and accelerating scenarios. Meanwhile, the proposed method can be well applied in a curve road driving environment.

Original languageEnglish
Article number9294138
Pages (from-to)225294-225305
Number of pages12
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • Autonomous vehicles
  • adaptive potential field
  • complex driving scenario
  • model predictive control
  • path planning

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