Introduces VZCrash, the largest public IMU dataset for ego-vehicle crashes, and shows through benchmarks that larger data scale improves crash detection models especially for real-world deployment.
Cognitive accident pre- diction in driving scenes: A multimodality benchmark
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
PaSBench-Video benchmark shows no tested MLLM exceeds 20% on strict proactive safety metrics, with recall correlated 0.64 to false-positive rate on safe clips.
VAGNet anticipates accidents in dashcam videos using global features from VideoMAE-V2 combined with transformers and graphs, reporting higher average precision and mean time-to-accident on four benchmarks while running more efficiently than prior methods.
citing papers explorer
-
VZCrash: A Large-Scale IMU Dataset of Ego-Vehicle Crashes
Introduces VZCrash, the largest public IMU dataset for ego-vehicle crashes, and shows through benchmarks that larger data scale improves crash detection models especially for real-world deployment.
-
VAGNet: Vision-based Accident Anticipation with Global Features
VAGNet anticipates accidents in dashcam videos using global features from VideoMAE-V2 combined with transformers and graphs, reporting higher average precision and mean time-to-accident on four benchmarks while running more efficiently than prior methods.