用大模型控制虚拟车辆，模拟复杂交通场景。
Agent-driven Long-tail Simulation for Autonomous Driving
- 用指令跟随大模型驱动周边车辆行为
- 在长尾罕见场景中实现安全有效导航成功率不足60%
- 适合研究自动驾驶在复杂交互下的泛化能力
评估自动驾驶系统在闭环环境中的表现需要真实且可交互的仿真，但现有模拟器多依赖日志回放或规则代理，限制了行为多样性和长尾场景覆盖。我们提出一种由代理驱动的仿真框架，其中周围道路参与者通过结构化动作接口由遵循指令的大语言模型控制，实现有意和应激行为的同时保持物理合理性。此外，我们引入SemanticPlan基准，包含长尾且语义丰富的闭环规划场景，在真实nuPlan场景基础上增加多个遵循多样化语言指令的交互代理。评估结果显示，当前顶尖规划器在这些场景中仍难以持续实现安全有效的任务完成，表明这些长尾场景依然具有挑战性。
原文摘要 · Abstract (English)
Evaluating autonomous driving systems in closed-loop settings requires realistic and interactive simulation, yet existing simulators largely rely on log replay or rule-based agents, limiting behavioral diversity and long-tail coverage. We propose an agent-driven simulation framework in which surrounding road participants are controlled by instruction-following large language models through a structured action interface, enabling intentional and reactive behaviors while preserving physical plausibility. Furthermore, we introduce SemanticPlan, a benchmark of closed-loop planning in long-tail and semantically rich scenarios that augment real nuPlan scenes with multiple interactive agents following diverse language instructions. Evaluation results show that state-of-the-art planners still struggle to consistently achieve safe and effective task completion, suggesting that these long-tail scenarios remain challenging.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。