Researchers have created a new AI system called MotoSafety to assess collision risk in two-wheeler riders under time pressure. The system uses a large dataset of labeled time-series sequences from simulator rides and incorporates the 'Learned Temporal Importance' principle. It achieves high accuracy and low latency, making it suitable for edge deployment on low-cost hardware. The authors also demonstrate its transferability to other domains such as human activity recognition and clinical diagnosis. --- Why it matters: This matters because AI systems can be used to improve safety in transportation systems, particularly in low- and middle-income countries where two-wheeler riders face significant risks. MotoSafety's ability to accurately assess collision risk under time pressure could inform the development of intelligent transportation systems that prioritize rider safety. Source: https://arxiv.org/abs/2608.17823