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ReactSim-Bench: Benchmarking Reactive Behavior World Model Simulation in Autonomous Driving
ReactSim-Bench is the first benchmark for systematicly evaluating the reactive capability of behavior world models in autonomous driving. It contains:
- Reactive closed-loop protocol with decoupled control. In ReactSim-Bench, The behavior world model controls the surrounding agents, while the autonomous vehicle (AV) are controled by its own policy instead of the world model.
- Customed AV behaviors beyond the log. ReactSim-Bench contains 2,636 scenarios with AV behaviors that differ from the log and create reactive pressure on surrounding agents. They are grouped into three categories: longitudinal,directional, and lateral deviations.
- Safety and feasibility metrics. ReactSim-Bench evaluates Agent-AV safety, agent-agent safety, map compliance, driving-direction compliance, and kinematic feasibility.
- Multiple baselines. We implement the Transformer-based (MTR), diffusion-based (CTG,VBD), and next-token-prediction-based (SMART, catk, Trajtok) behavior world models on ReactSim-Bench as baselines.
This dataset is based on nuPlan. Each file is named as <map_name>_<scene_token>_<sample_token>.pkl. This dataset only contains the customed behaviors of autonomous vehicles, without any information in original nuPlan scenarios (states of agents, HD maps or sensors).
For the nuPlan scenarios, please download from its official website.
For more information, please visit our GitHub repository: https://github.com/Thinklab-SJTU/ReactSim-Bench.
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