arXiv Computer Vision
From Wrecks to Wisdom: Recovering Crash Mechanics from Real-World Multi-View Photos
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arXiv AI
Jul 7
Responsibility Distribution Estimation in Ego-View Accident Videos with Multimodal Large Language Models
arXiv:2607. 03591v1 Announce Type: cross Abstract: Recent studies on multimodal traffic accident understanding have mainly relied on infrastructure-camera footage, satellite imagery, or structured crash records.
arXiv AI
Jun 11
Metadata-Aware Multi-Prompt Reasoning for Zero-Shot Accident Understanding
arXiv:2606. 12047v1 Announce Type: cross Abstract: In this paper, we address the problem of zero-shot understanding of accidents from surveillance videos by identifying when an impact event occurs, what type of impact it is, and where in the frame it occurs using natural language.
Hugging Face Trending Papers
Jul 9
Mixture of Enhanced-View Experts for Multi-Query Vehicle ReID and A Large-Scale Benchmark
Multi-query vehicle ReID aims to leverage complementary information from diverse views for robust feature learning. However, current methods suffer from simplistic feature fusion and thus easily ignores some important view information and cross-view relationships.
arXiv AI
Sep 7
AccidentSim: Generating Vehicle Collision Videos with Physically Realistic Collision Trajectories from Real-World Accident Reports
AccidentSim is a framework that generates physically realistic vehicle collision videos by extracting physical clues from real-world accident reports. It uses a reliable physical simulator to replicate post-collision trajectories, builds a trajectory dataset, fine‑tunes a language model to predict consistent trajectories from user prompts, and finally renders high‑quality videos with Neural Radiance Fields. The resulting videos show strong visual and physical authenticity compared to existing methods.
arXiv Computer Vision
Aug 21
CAViAR: A Causal Video Dataset for Fine-Grained Accident Reasoning in Real-World Scenarios
arXiv:2608. 19380v1 Announce Type: new Abstract: While modern autonomous driving systems excel at perception tasks such as object detection and trajectory prediction, they lack the high-level causal reasoning required to interpret traffic accidents.
arXiv AI
Sep 1
SynCrash: A Multi-Stage Pipeline for Zero-Shot Accident Detection and Localization in Traffic Surveillance Video
arXiv:2608.29759v1 Announce Type: cross Abstract: We present SynCrash, a multi-stage pipeline for zero-shot accident detection, spatial localization, and collision-type classification in fixed-view C...