  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.
