Safe Tactical Decision-making for Autonomous Vehicles Considering Uncertainties in Deep Learning-based Visual Perception

Tactical decision-making plays a key role in ensuring the operational safety of autonomous vehicle (AV) systems. Recent environment perception techniques are built upon the deep learning (DL) approaches and utilize probabilistic representations to quantitatively capture the inherent uncertainties in networks. The downstream decision-making module shall utilize the perceived probabilistic information to derive proper maneuver decisions. However, how to translate the uncertainties of DL between the two stages to manage the risk remains an open problem. This paper proposes a holistic and interpretable framework that can explicitly represent uncertainties of DL-based perception and generate safe maneuvers by accounting for such uncertainties. By quantifying and propagating uncertainties of perceptions networks, an explicit link is established between DL perception uncertainty and safety utility of decision-making via the Adaptive Likelihood mechanism. Built on this basis, we propose an uncertainty-aware decision-making approach (PUALD-Net) that can incorporates both regression and classification perception uncertainties into probabilistic reasoning and derive risk-bounding decisions. Real-world field test results demonstrate that the proposed framework can effectively ensure safety, and outperforms existing approaches in terms of safety and driving capability metrics (Infraction Rate and Driving Score) in adverse scenarios incurring high perception uncertainties.