Interactive Planning for Autonomous Urban Driving in Adversarial Scenarios

Abstract

Autonomous urban driving among human-driven cars requires a holistic understanding of road rules, driver intents and driving styles. This is challenging as a short-term, single instance, driver intent of lane change may not correspond to their driving styles for a longer duration. This paper presents an interactive behavior planner which accounts for road context, short-term driver intent, and long-term driving style to infer beliefs over the latent states of surrounding vehicles. We use a specialized Partially Observable Markov Decision Process to provide risk-averse decisions. Specifically, we consider adversarial driving scenarios caused by irrational drivers to validate the robustness of our proposed interactive behavior planner in simulation as well as on a full-size self-driving car. Our experimental results show that our algorithm enables safer and more travel time-efficient autonomous driving compared to baselines even in adversarial scenarios.