Simultaneous Feasible Local Planning and Path-Following Control for Autonomous Driving
In this paper, a new approach for lane change and double-lane change planning and following for autonomous driving is proposed. Herein, the authors introduce a novel technique, based on exponential functions, to generate online and feasible lane change maneuvers; these maneuvers satisfy the constraints on the maximum allowable curvature a given vehicle can handle. In addition, a simultaneous local path planning and path-following control framework is adapted. The framework utilizes a multi-threading architecture to run the local planner module concurrently with the control module. The planning module generates parametric reference paths based on the proposed lane change and double-lane change maneuvers. The control module is based on a Model Predictive Path-following Control (MPFC) scheme, which achieves the path following objective while satisfying vehicle’s state and control limits. To validate the proposed framework, several real-time simulation scenarios are designed and tested on CARLA soft real-time vehicle simulator. The results show the effectiveness of the proposed framework in generating and smoothly following lane change maneuvers.
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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 © 2022, IEEE.
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Authors:
- Daoud, Mohamed A
- Mehrez, Mohamed W
- Rayside, Derek
- Melek, William W
- Publication Date: 2022-9
Language
- English
Media Info
- Media Type: Web
- Pagination: pp 16358-16370
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 23
- 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: Autonomous vehicles; Lane changing; Trajectory control
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01868925
- Record Type: Publication
- Files: TRIS
- Created Date: Dec 29 2022 9:11AM