Collision-Probability-Aware Human-Machine Cooperative Planning for Safe Automated Driving

Chao Huang, Peng Hang, Zhongxu Hu, Chen Lv

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

In this paper, we investigate a novel collision probability-aware human-machine cooperative planning and tracking method for enhancing safety of automated vehicles. Firstly, a long-term trajectory prediction is obtained by using Gaussian mixture models with vehicle historical data. After that, a novel risk assessment system based on the dynamic potential field (DPF) and the fuzzy inference system (FIS) is proposed to evaluate the risk level of the human driver's behaviors. Based on the assessed human driving risk, the human-machine cooperation is activated adaptively during the trajectory planning. A novel human-machine cooperative trajectory planning algorithm, named as HM-pRRT, is proposed and used to incorporate the driver's intent and automation's corrective actions during trajectory planning. Testing results show that the proposed HM-pRRT algorithm is able to simultaneously mitigate collision and provide a safe trajectory, effectively ensuring the safety of the vehicle and reducing conflicts during human-machine interactions.

Original languageEnglish
Pages (from-to)9752-9763
Number of pages12
JournalIEEE Transactions on Vehicular Technology
Volume70
Issue number10
DOIs
Publication statusPublished - 1 Oct 2021
Externally publishedYes

Keywords

  • automated driving
  • collision mitigation
  • cooperative trajectory planning
  • HM-RRT
  • Human-machine collaboration
  • risk assessment

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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