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 among multiple vehicles. To support the testing and refinement of AV policies, simulating safety-critical traffic events is an essential challenge to be addressed. In this work, we introduce TrafficGamer, which facilitates game-theoretic traffic simulation by viewing common road driving as a multi-agent game. In evaluating the empirical performance across various real-world datasets, TrafficGamer ensures both fidelity and exploitability of the simulated scenarios, guaranteeing that they not only statically align with real-world traffic distribution but also efficiently capture equilibriums for representing safety-critical scenarios involving multiple agents. Additionally, the results demonstrate that TrafficGamer exhibits highly flexible simulation across various contexts. Specifically, we demonstrate that by configuring risk-sensitive constraints during optimization, the generated scenarios can dynamically adapt to equilibriums of varying tightness. To the best of our knowledge, TrafficGamer is the first simulator capable of generating diverse traffic scenarios involving multiple agents.

Note: From bottom to top, the constraint is stronger; From left to right, the risk is higher.

Argoverse 2 Y-Junction Scenario

2D Visualization (third-person perspective)

3D Visualization (first-person perspective)

Argoverse 2 Dense Intersection Scenario

2D Visualization (third-person perspective)

3D Visualization (first-person perspective)

Argoverse 2 Roundabout Scenario

2D Visualization (third-person perspective)

3D Visualization (first-person perspective)

Argoverse 2 Dual Intersection Scenario

2D Visualization (third-person perspective)

3D Visualization (first-person perspective)

Argoverse 2 T-Junction Scenario

2D Visualization (third-person perspective)

3D Visualization (first-person perspective)

Argoverse 2 Merge Scenario

2D Visualization (third-person perspective)

3D Visualization (first-person perspective)

Waymo Motion Merge Scenario

2D Visualization (third-person perspective)

3D Visualization (first-person perspective)

Waymo Motion Dual Intersection Scenario

2D Visualization (third-person perspective)

3D Visualization (first-person perspective)

Waymo Motion T-Junction Scenario

2D Visualization (third-person perspective)

3D Visualization (first-person perspective)

Waymo Motion Dense Intersection Scenario

2D Visualization (third-person perspective)

3D Visualization (first-person perspective)

Waymo Motion Roundabout Scenario

2D Visualization (third-person perspective)

3D Visualization (first-person perspective)

Waymo Motion Y-Junction scenario

2D Visualization (third-person perspective)

3D Visualization (first-person perspective)

@article{qiao2024trafficgamer,
  title={TrafficGamer: Reliable and Flexible Traffic Simulation for Safety-Critical Scenarios with Game-Theoretic Oracles},
  author={Qiao Guanren and Quan Guorui and Yu Jiawei and Jia Shujun and Liu Guiliang},
  journal={arXiv preprint arXiv:2408.15538},
  year={2024}
}