Decision Making in Urban Traffic: A Game Theoretic Approach for Autonomous Vehicles Adhering to Traffic Rules

TL;DR

This paper introduces a game-theoretic framework for autonomous vehicle decision-making in urban traffic, enabling safe, rule-compliant navigation through complex interactions with unpredictable traffic participants.

Contribution

It presents a novel differential game approach that models traffic interactions and derives Nash equilibrium solutions for autonomous vehicle decision-making.

Findings

Successfully tested in simulation and real-world platform

Autonomous vehicle safely interacts with traffic while following rules

Framework effectively manages complex traffic interactions

Abstract

One of the primary challenges in urban autonomous vehicle decision-making and planning lies in effectively managing intricate interactions with diverse traffic participants characterized by unpredictable movement patterns. Additionally, interpreting and adhering to traffic regulations within rapidly evolving traffic scenarios pose significant hurdles. This paper proposed a rule-based autonomous vehicle decision-making and planning framework which extracts right-of-way from traffic rules to generate behavioural parameters, integrating them to effectively adhere to and navigate through traffic regulations. The framework considers the strong interaction between traffic participants mathematically by formulating the decision-making and planning problem into a differential game. By finding the Nash equilibrium of the problem, the autonomous vehicle is able to find optimal decisions. The proposed framework was tested under simulation as well as full-size vehicle platform, the results show that the ego vehicle is able to safely interact with surrounding traffic participants while adhering to traffic rules.