TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles

TL;DR

TrafficGamer introduces a game-theoretic multi-agent traffic simulation method that accurately models safety-critical scenarios, ensuring high fidelity, diversity, and adaptability for autonomous vehicle safety testing.

Contribution

It presents a novel game-theoretic framework for traffic simulation that effectively captures safety-critical scenarios with high fidelity and flexibility, outperforming existing methods.

Findings

Ensures fidelity, exploitability, and diversity in simulated scenarios

Accurately captures equilibria for safety-critical multi-agent interactions

Provides adaptable simulations with risk-sensitive constraints

Abstract

While modern Autonomous Vehicle (AV) systems can develop reliable driving policies under regular traffic conditions, they frequently struggle with safety-critical traffic scenarios. This difficulty primarily arises from the rarity of such scenarios in driving datasets and the complexities associated with predictive modeling of multiple vehicles. Effectively simulating safety-critical traffic situations is therefore a crucial challenge. In this paper, we introduce TrafficGamer, which facilitates game-theoretic traffic simulation by viewing common road driving as a multi-agent game. When we evaluate the empirical performance across various real-world datasets, TrafficGamer ensures both the fidelity, exploitability, and diversity of the simulated scenarios, guaranteeing that they not only statically align with real-world traffic distribution but also efficiently capture equilibria for representing safety-critical scenarios involving multiple agents compared with other methods. Additionally, the results demonstrate that TrafficGamer provides highly flexible simulations across various contexts. Specifically, we demonstrate that the generated scenarios can dynamically adapt to equilibria of varying tightness by configuring risk-sensitive constraints during optimization. We have provided a demo webpage at: https://anonymous.4open.science/api/repo/trafficgamer-demo-1EE0/file/index.html.