Human–Machine Adaptive Shared Control for Safe Driving Under Automation Degradation
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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/19391390
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Supplemental Notes:
- Copyright © 2022, IEEE.
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Authors:
- Huang, Chao
- Lv, Chen
- Hang, Peng
- Hu, Zhongxu
- Xing, Yang
- Publication Date: 2022-4
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 53-66
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Serial:
- IEEE Intelligent Transportation Systems Magazine
- Volume: 14
- Issue Number: 2
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1939-1390
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5117645
Subject/Index Terms
- TRT Terms: Adaptive control; Automation; Driving; Human machine systems; Level 3 driving automation; Traffic safety
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01842135
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 11 2022 10:46AM