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This paper proposes a proof-of-concept AI pipeline that uses multi-modal data (images and accident reports) to assess railway crossing safety, achieving a macro F1 score of 0.757 for risk classification and an RMSE of 0.078 for safety score estimation using a fine-tuned compact VLM.
MCBench is a new benchmark for assessing the safety of omnimodal large language models across vision, audio, and text modalities. It includes 1196 scenarios and finds current models struggle with cross-modal safety reasoning.
This paper proposes a multi-agent reinforcement learning framework that co-trains an autonomous vehicle and pedestrians with personality-driven jaywalking behavior, achieving a 30% reduction in collisions compared to single-agent approaches and demonstrating more realistic interaction scenarios.