Safe motion planning for autonomous vehicles by quantifying uncertainties of deep learning-enabled environment perception

Li, D., Liu, B., Huang, Z., Hao, Q., Zhao, D. and Tian, B. (2023) Safe motion planning for autonomous vehicles by quantifying uncertainties of deep learning-enabled environment perception. IEEE Transactions on Intelligent Vehicles, 9(1), pp. 2318-2332. (doi: 10.1109/TIV.2023.3297735)

Abstract

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 paper 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.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Natural Science Foundation of China under Grants 52272419 and 62261160654, in part by the Science and Technology Innovation Committee of Shenzhen City under Grants JCYJ20200109141622964 and JCYJ20220818103006012, and in part by the National Key Research and Development Program of China under Grant 2022YFB4703700.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhao, Dr Dezong
• Authors: Li, D., Liu, B., Huang, Z., Hao, Q., Zhao, D., and Tian, B.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Transactions on Intelligent Vehicles
• Publisher: IEEE
• ISSN: 2379-8858
• ISSN (Online): 2379-8904
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Transactions on Intelligent Vehicles 9(1): 2318-2332
• Publisher Policy: Reproduced in accordance with the publisher copyright policy