Learning Autonomous Control Policy for Intersection Navigation With Pedestrian Interaction
In recent years, great efforts have been devoted to deep imitation learning for autonomous driving control, where raw sensory inputs are directly mapped to control actions. However, navigating through densely populated intersections remains a challenging task due to uncertainty caused by uncertain traffic participants. The authors focus on autonomous navigation at crowded intersections that require interaction with pedestrians. A multi-task conditional imitation learning framework is proposed to adapt both lateral and longitudinal control tasks for safe and efficient interaction. A new benchmark called IntersectNav is developed and human demonstrations are provided. Empirical results show that the proposed method can achieve a success rate gain of up to 30% compared to the state-of-the-art.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
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Supplemental Notes:
- Copyright © 2023, IEEE.
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Authors:
- Zhu, Zeyu
- Zhao, Huijing
- Publication Date: 2023-5
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References;
- Pagination: pp 3270-3284
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Serial:
- IEEE Transactions on Intelligent Vehicles
- Volume: 8
- Issue Number: 5
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 2379-8858
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857
Subject/Index Terms
- TRT Terms: Automated vehicle control; Autonomous vehicles; Intersections; Machine learning; Pedestrians; Task analysis
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Pedestrians and Bicyclists; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01909396
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 22 2024 4:14PM