TRACE: Topology-aware Reconstruction of Accidents in CARLA for AV Evaluation
Nahian Salsabil, Sebastian Elbaum
TLDR
TRACE reconstructs real-world accident reports into high-fidelity CARLA simulations, enabling robust AV evaluation against rare, safety-critical scenarios.
Key contributions
- Retrieves site-specific OpenStreetMap data to preserve exact road topology.
- Leverages LLMs to infer initial vehicle states from road geometry and maneuvers.
- Generates simulation trajectories from semi-structured report data.
- Curated a benchmark of 52 diverse accident scenarios for AV testing.
Why it matters
This paper addresses the critical need for validating AVs against rare, safety-critical scenarios by automating accident reconstruction. It provides a valuable open-source benchmark for robust AV system evaluation, improving safety and reliability.
Original Abstract
Validating Autonomous Vehicles (AVs) requires exposure to rare, safety-critical scenarios, infrequent in routine driving data. Existing benchmarks address this by generating synthetic conflicts or mapping accident descriptions to abstract road geometries, failing to capture the topological complexity of real-world crashes. We introduce TRACE , a pipeline that automates the reconstruction of NHTSA crash reports into high-fidelity CARLA simulations by (1) retrieving site-specific OpenStreetMap data to preserve exact road topology, (2) leveraging Large Language Models to infer vehicles' initial state from road geometry and pre-crash maneuvers, and (3) generating simulation trajectories from semi-structured report data. Using this pipeline, we curated a benchmark of 52 diverse accident scenarios covering varied collision types, road topologies, and pre-crash maneuvers, providing a challenging open source resource for testing AV systems against real-world failures.
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