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

TL;DR

The paper tackles urban autonomous-vehicle decision-making under complex interactions and dynamic traffic rules. It introduces a rule-adherent decision framework that extracts right-of-way from traffic regulations and translates it into behavior parameters $\gamma$ within a differential game, whose Nash equilibrium yields the ego vehicle's optimal path and speed. Key contributions include a generalized traffic-rule interpretation across scenarios (e.g., all-way stop signs, T-junctions, crosswalks), integration of these rules into a linearized-quadratic game via a behavior-parameter vector $H(\gamma)$, and validation through simulations and a real-world shuttle-bus platform with real-time performance at 10 Hz. The framework demonstrates safe, rule-compliant interactions with surrounding traffic, suggesting a scalable approach for rule-aware planning in complex urban environments.

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.

Figures (13)

• Figure 1: Naturalistic autonomous intersection handling framework
• Figure 2: Traffic participants' minimum distances distribution
• Figure 3: Right-of-way extraction from typical traffic scenarios
• Figure 4: Uncontrolled intersection simulation results
• Figure 5: Intersection with yield signs' simulation result