Interactive Decision Making for Autonomous Vehicles in Dense Traffic

Interactive Decision Making for Autonomous V ehicles in Dense Traffic David Isele 1 Abstract -- Dense urban traffic environments can produce situations where accurate prediction and dynamic models are insufficient for successful autonomous vehicle motion planning. We investigate how an autonomous agent can safely negotiate with other traffic participants, enabling the agent to handle potential deadlocks. Specifically we consider merges where the gap between cars is smaller than the size of the ego vehicle. We propose a game theoretic framework capable of generating and responding to interactive behaviors. Our main contribution is to show how game-tree decision making can be executed by an autonomous vehicle, including approximations and reasoning that make the tree-search computationally tractable. Additionally, to test our model we develop a stochastic rule-based traffic agent capable of generating interactive behaviors that can be used as a benchmark for simulating traffic participants in a crowded merge setting. I NTRODUCTION Much of the long tail around autonomous driving behavior relates to complex interactions between self-interested agents. Since other traffic participants exhibit a great deal of variety and are often neither purely adversarial, nor purely cooperative, it can be difficult to reason about their behavior. However this type of reasoning is essential to numerous traffic situations in congested traffic such as overcrowded merge scenarios depicted in Figure 1.