Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles

• URL: http://arxiv.org/abs/2108.05118v1
• Date: Wed, 11 Aug 2021 09:41:54 GMT
• Title: Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles
• Abstract summary: Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles. We propose a safe motion planning framework featuring the quantification and propagation of DNN-based perception uncertainties and motion uncertainties.
• Score: 16.598370537756068
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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