Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles
Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles. In this paper, we propose a safe motion planning framework featuring the quantification and propagation of DNN-based perception uncertainties and motion uncertainties. Contributions of this work are twofold: (1) A Bayesian Deep Neural network model which detects 3D objects and quantitatively captures the associated aleatoric and epistemic uncertainties of DNNs; (2) An uncertainty-aware motion planning algorithm (PU-RRT) that accounts for uncertainties in object detection and ego-vehicle's mot
Resource information
- Authors
- Ding, Liuhui, Li, Dachuan, Liu, Bowen, Lan, Wenxing, Bai, Bing, Hao, Qi, Cao, Weipeng, Pei, Ke
- Research area
- Artificial intelligence
- Research topics
- Robotics (cs.RO), Artificial Intelligence (cs.AI), Systems and Control (eess.SY), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, I.2.9
- Publication date
- 2021
- DOI
- 10.48550/arxiv.2108.05118
- Upstream last modified
- Feb 20, 2022
- License
- Creative Commons Attribution Non Commercial No Derivatives 4.0 International
- Last record update
- Nov 4, 2026
About this resource
Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles. In this paper, we propose a safe motion planning framework featuring the quantification and propagation of DNN-based perception uncertainties and motion uncertainties. Contributions of this work are twofold: (1) A Bayesian Deep Neural network model which detects 3D objects and quantitatively captures the associated aleatoric and epistemic uncertainties of DNNs; (2) An uncertainty-aware motion planning algorithm (PU-RRT) that accounts for uncertainties in object detection and ego-vehicle's motion. The proposed approaches are validated via simulated complex scenarios built in CARLA. Experimental results show that the proposed motion planning scheme can cope with uncertainties of DNN-based perception and vehicle motion, and improve the operational safety of autonomous vehicles while still achieving desirable efficiency.