Decision Making for Autonomous Vehicles at Unsignalized Intersection in Presence of Malicious Vehicles

TL;DR

This paper proposes a game-theoretic decision-making approach for autonomous vehicles at unsignalized intersections, accounting for malicious vehicles that ignore traffic rules, using Nash equilibrium strategies based on vehicle beliefs.

Contribution

It introduces a novel game-theoretic framework that models malicious behavior in autonomous vehicle decision making at intersections, incorporating belief-based priority determination.

Findings

The method effectively identifies safe maneuvers in the presence of malicious vehicles.

Simulations demonstrate the approach's robustness against different malicious vehicle scenarios.

The framework can be extended to more complex traffic environments.

Abstract

In this paper, we investigate the decision making of autonomous vehicles in an unsignalized intersection in presence of malicious vehicles, which are vehicles that do not respect the law by not using the proper rules of the right of way. Each vehicle computes its control input as a Nash equilibrium of a game determined by the priority order based on its own belief: each of non-malicious vehicle bases its order on the law, while a malicious one considers itself as having priority. To illustrate our method, we provide numerical simulations, with different scenarios given by different cases of malicious vehicles.