Traffic-Context-Aware Driver Emotion Recognition with Expectation-Deviation-Driven Inference

Longhao Yan, Jinhao Liang, Kaidi Yang

Driver emotion provides an important indication of how drivers perceive and evaluate ongoing traffic situations, making reliable emotion recognition an important capability for effective human-vehicle interaction. Yet, most existing driver emotion recognition approaches rely primarily on in-cabin images and overlook how surrounding traffic interactions shape driver emotional responses. To address this limitation, we propose a traffic-context-aware driver emotion recognition (TADER) framework that integrates in-cabin signals with out-of-cabin traffic scenarios, modeling driver emotions as arising from expectation deviation represented by the discrepancy between predicted and observed vehicle trajectories. The proposed framework comprises two key components: (1) a vision-based base recognizer that extracts spatiotemporal representations from in-cabin facial images to infer driver emotions, and (2) a traffic-context-aware fusion module that quantifies expectation deviation from external traffic scenarios and integrates it into the base recognizer via attention-based embedding fusion. To overcome the scarcity of paired in-cabin and out-of-cabin data, we introduce a transfer learning…