Safe Motion Planning for Autonomous Vehicles by Quantifying Uncertainties of Deep Learning-Enabled Environment Perception | |
Li, Dachuan5; Liu, Bowen4; Huang, Zijian4; Hao, Qi4,5; Zhao, Dezong3; Tian, Bin1,2 | |
刊名 | IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
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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 |
DOI | 10.1109/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 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:001173317800192 |
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 |
内容类型 | 期刊论文 |
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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Glasgow, James Watt Sch Engn, Glasgow G12 8QQ, Lanark, Scotland 4.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518057, Peoples R China 5.Southern Univ Sci & Technol, Res Inst Trustworthy Autonomous Syst, Shenzhen 518055, 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. |
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