Agent-driven Long-tail Simulation for Autonomous Driving
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 fol...
| Authors | Junru Gu, Lijin Yang, Jianing Huang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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