Decision Making for Connected Automated Vehicles at Urban Intersections Considering Social and Individual Benefits

TL;DR

This paper presents a game-theoretic decision-making framework for connected automated vehicles at urban intersections, optimizing safety, efficiency, and social benefits through fuzzy coalitional games and risk assessment.

Contribution

It introduces a novel fuzzy coalitional game approach incorporating social and individual benefits for CAV decision-making at intersections.

Findings

Framework improves traffic safety and efficiency.

Effective risk assessment with Gaussian potential field.

Validated through three test cases.

Abstract

To address the coordination issue of connected automated vehicles (CAVs) at urban scenarios, a game-theoretic decision-making framework is proposed that can advance social benefits, including the traffic system efficiency and safety, as well as the benefits of individual users. Under the proposed decision-making framework, in this work, a representative urban driving scenario, i.e. the unsignalized intersection, is investigated. Once the vehicle enters the focused zone, it will interact with other CAVs and make collaborative decisions. To evaluate the safety risk of surrounding vehicles and reduce the complexity of the decision-making algorithm, the driving risk assessment algorithm is designed with a Gaussian potential field approach. The decision-making cost function is constructed by considering the driving safety and passing efficiency of CAVs. Additionally, decision-making constraints are designed and include safety, comfort, efficiency, control and stability. Based on the cost function and constraints, the fuzzy coalitional game approach is applied to the decision-making issue of CAVs at unsignalized intersections. Two types of fuzzy coalitions are constructed that reflect both individual and social benefits. The benefit allocation in the two types of fuzzy coalitions is associated with the driving aggressiveness of CAVs. Finally, the effectiveness and feasibility of the proposed decision-making framework are verified with three test cases.