Decision-Making Driven by Driver Intelligence and Environment Reasoning for High-Level Autonomous Vehicles: A Survey
Autonomous vehicle (AV) is expected to reshape the future transportation system, and its decision-making is one of the most critical modules. Many current decision-making modules are designed scenario-by-scenario and thus are not capable of meeting high-level AV requirements for lack of scalability to cope with the diversity of drivers’ demands and the infinity of traffic elements. This paper surveys the decision-making design inspired by driver intelligence and environment reasoning with better scalability for future high-level AVs. It involves three aspects: human factors in driving, environment reasoning, and detailed decision methods. The current state of the art of these three sections is surveyed in this paper. The characteristics of distinguished drivers, their decision mechanism, and the factors influencing decision-making are reviewed first to learn from excellent drivers. Environment reasoning is introduced following as a three-layer structure consisting of restriction, interaction, and attention. The optimization-based decision-making algorithms are then reviewed from the aspect of optimization targets, frameworks, applied scenarios, and limitations. Inspired by the existing research on driver intelligence and environment reasoning, a promising decision-making framework is also introduced for high-level AV design.
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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 © 2023, IEEE.
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Authors:
- Wang, Yuning
- Jiang, Junkai
- Li, Shangyi
- Li, Ruochen
- Xu, Shaobing
- Wang, Jianqiang
- Li, Keqiang
- Publication Date: 2023-10
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 10362-10381
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 24
- Issue Number: 10
- 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; Decision making; Driver performance; Human factors; Intelligence; Traffic characteristics
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01903881
- Record Type: Publication
- Files: TRIS
- Created Date: Jan 2 2024 9:19AM