InCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring
arXiv:2607.14683v1 Announce Type: new Abstract: Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction. However, existing public datasets for in-cabin affective computing are largely limited