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Safe Motion Planning for Autonomous Vehicles by Quantifying Uncertainties of Deep Learning-Enabled Environment Perception

文献类型：期刊论文

作者Li, Dachuan1; Liu, Bowen2; Huang, Zijian2; Hao, Qi1,2; Zhao, Dezong3; Tian, Bin4,5
刊名IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
出版日期2024
卷号9期号:1页码:2318-2332
关键词Uncertainty Planning Three-dimensional displays Decision making Detectors Trajectory Probabilistic logic Autonomous driving motion planning object detection Bayesian deep learning uncertainty quantification
ISSN号2379-8858
DOI10.1109/TIV.2023.3297735
通讯作者Hao, Qi(haoq@sustech.edu.cn)
英文摘要Conventional perception-planning pipelines of autonomous vehicles (AV) utilize deep learning (DL) techniques that typically generate deterministic outputs without explicitly evaluating their uncertainties and trustworthiness. Therefore, the downstream decision-making components may generate unsafe outputs leading to system failure or accidents, if the preceding perception component provides highly uncertain information. To mitigate this issue, this article proposes a coherent safe perception-planning framework that quantifies and transfers DL-based perception uncertainties. Following the Bayesian Deep Learning paradigm, we design a probabilistic 3D object detector that extracts objects from LiDAR point clouds while quantifying the corresponding aleatoric and epistemic uncertainty. A chance-constrained motion planner is designed to formulate an explicit link between DL-based perception uncertainties and operation risk and generate safe and risk-bounding trajectories. The proposed framework is validated through various challenging scenarios in the CARLA simulator. Experiment results demonstrate that our framework can effectively capture the uncertainties in DL, and generate trajectories that bound the risk under DL perception uncertainties. It also outperforms counterpart approaches without explicitly evaluating the uncertainties of DL-based perception.
资助项目National Natural Science Foundation of China[52272419] ; National Natural Science Foundation of China[62261160654] ; Science and Technology Innovation Committee of Shenzhen City[JCYJ20200109141622964] ; Science and Technology Innovation Committee of Shenzhen City[JCYJ20220818103006012] ; National Key Research and Development Program of China[2022YFB4703700]
WOS研究方向Computer Science ; Engineering ; Transportation
语种英语
WOS记录号WOS:001173317800192
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; Science and Technology Innovation Committee of Shenzhen City ; National Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/58777]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Hao, Qi
作者单位1.Southern Univ Sci & Technol, Res Inst Trustworthy Autonomous Syst, Shenzhen 518055, Peoples R China
2.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518057, Peoples R China
3.Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Dachuan,Liu, Bowen,Huang, Zijian,et al. Safe Motion Planning for Autonomous Vehicles by Quantifying Uncertainties of Deep Learning-Enabled Environment Perception[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2024,9(1):2318-2332.
APA Li, Dachuan,Liu, Bowen,Huang, Zijian,Hao, Qi,Zhao, Dezong,&Tian, Bin.(2024).Safe Motion Planning for Autonomous Vehicles by Quantifying Uncertainties of Deep Learning-Enabled Environment Perception.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,9(1),2318-2332.
MLA Li, Dachuan,et al."Safe Motion Planning for Autonomous Vehicles by Quantifying Uncertainties of Deep Learning-Enabled Environment Perception".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 9.1(2024):2318-2332.

入库方式： OAI收割

来源：自动化研究所

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