Interactive Decision Making for Autonomous Vehicles in Dense Traffic

TL;DR

This paper presents a game-theoretic decision-making framework for autonomous vehicles to safely navigate dense traffic, especially during merges, by modeling interactive behaviors and ensuring computational feasibility.

Contribution

It introduces a novel game-tree decision-making approach for autonomous vehicles and a stochastic traffic agent for benchmarking in dense traffic scenarios.

Findings

Game-tree decision making is feasible with approximations.

The framework enables safe negotiation in tight merges.

A benchmark traffic agent was developed for simulation.

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.