AUG 18, 2026 · PREPRINT
MotoSafety: Edge-AI with Learned Temporal Importance for Two-Wheeler Collision Risk Assessment Under Time Pressure
arXiv
A simulator-based feasibility study introducing a novel machine-learning architecture for collision risk prediction in a safety domain, with no clinical or real-world validation, no peer review, and no comparison to established clinical risk stratification.
Reported
Accuracy94.97%
ROC AUC99.33%
Mean squared error (forecasting)0.039 MSE