Human Machine Adaptive Shared Control for Safe Driving Under Automation Degradation

Chao Huang, Chen Lv, Peng Hang, Zhongxu Hu, Yang Xing

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

In this article, a human–machine adaptive shared control method is proposed for automated vehicles (AVs) under automation performance degradation. First, a novel risk assessment module is proposed to monitor driving behavior and evaluate automation performance degradation for AVs. Then, an adaptive control authority allocation module is developed. In the event of any performance degradation, the control authority allocated to the automation system is decreased based on the assessed risk. Consequently, the control authority allocated to a human driver is adaptively increased and thus requires more driver engagement in the control loop to compensate for the automation degradation and ensure the vehicle’s safety. Experimental validation is conducted under different driving scenarios. The test results show that the approach can effectively compensate for vehicle automation performance degradation through human–machine adaptive shared control, ensuring the safety of automated driving.
Original languageEnglish
Pages (from-to)53-66
Number of pages14
JournalIEEE Intelligent Transportation Systems Magazine
Volume14
Issue number2
DOIs
Publication statusPublished - Mar 2022
Externally publishedYes

Keywords

  • Automation
  • Degradation
  • Resource management
  • Risk management
  • Safety
  • Trajectory
  • Vehicles

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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