长期交通仿真与交错自回归运动及场景生成

摘要

理想的交通模拟器应能复现自动驾驶系统在部署过程中所经历的真实长期点对点行程。现有模型和基准主要关注场景中初始智能体的闭环运动模拟，这在长期模拟中存在明显不足：随着自车进入新区域，智能体会不断进出场景。为此，我们提出了InfGen，一个统一的下一令牌预测模型，它能够交替执行闭环运动模拟与场景生成。InfGen可自动在闭环运动模拟与场景生成模式间切换，从而实现稳定的长期推演模拟。在短期（9秒）交通模拟中，InfGen达到了业界领先水平；而在长期（30秒）模拟中，其表现显著优于所有其他方法。InfGen的代码与模型将在https://orangesodahub.github.io/InfGen 发布。

English

An ideal traffic simulator replicates the realistic long-term point-to-point trip that a self-driving system experiences during deployment. Prior models and benchmarks focus on closed-loop motion simulation for initial agents in a scene. This is problematic for long-term simulation. Agents enter and exit the scene as the ego vehicle enters new regions. We propose InfGen, a unified next-token prediction model that performs interleaved closed-loop motion simulation and scene generation. InfGen automatically switches between closed-loop motion simulation and scene generation mode. It enables stable long-term rollout simulation. InfGen performs at the state-of-the-art in short-term (9s) traffic simulation, and significantly outperforms all other methods in long-term (30s) simulation. The code and model of InfGen will be released at https://orangesodahub.github.io/InfGen

长期交通仿真与交错自回归运动及场景生成

Long-term Traffic Simulation with Interleaved Autoregressive Motion and Scenario Generation

摘要

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