Tomaso Trinci

Scaling data volume is crucial for training effective crash detection models, with implications for real-world deployment success.

MLLMs can learn to identify and explain safety-critical driving events with significantly improved accuracy by fusing multimodal sensor data and fine-tuning on targeted pseudo-labels, all while using fewer than 50M trainable parameters.