Uncertainty-Aware Deep Imitation Learning and Deployment for Autonomous Navigation through Crowded Intersections

Navigation through crowded intersections is a challenge for autonomous vehicles, where uncertainty arises from interaction with other road users, encountering new scenes and weathers, etc. Recent end-to-end autonomous control deep models learned from human drivers have shown promising driving performance, whereas they are not as transparent and safe as traditional rule-based systems. When facing situations that they are unfamiliar with or uncertain about, the deep models’ predictions could be unsafe and untrustworthy. With- out the ability to identify these situations and issue warnings beforehand, cascading errors of deep models may result in catastrophes. Therefore, this work combines the strengths of both data-driven and traditional rule-based approaches to achieve better driving quality and safety. We propose a hetero- geneity uncertainty quantification method based on imitation learning, where both data and model uncertainties of the lateral and longitudinal control tasks are quantified. We also propose a policy deployment strategy where a safety indicator is developed upon estimated uncertainty to bridge the data-driven performance layer and the rule-based fallback layer. We learned from human driving demonstrations and conducted extensive closed-loop tests. Results demonstrate the effectiveness and importance of the proposed uncertainty quantification method and policy deployment strategy.