Crash Mitigation in Motion Planning for Autonomous Vehicles
A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2019, IEEE.
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Authors:
- Wang, Hong
- Huang, Yanjun
- Khajepour, Amir
- Rasekhipour, Yadollah
- Zhang, Yubiao
- Cao, Dongpu
- Publication Date: 2019-9
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: pp 3313-3323
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 20
- Issue Number: 9
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Automated vehicle control; Automatic tracking; Autonomous vehicles; Crash analysis; Crash avoidance systems; Crash risk forecasting; Mathematical prediction; Traffic crashes; Trajectory control
- Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01718385
- Record Type: Publication
- Files: TLIB, TRIS
- Created Date: Sep 27 2019 12:07PM