DriverEmo

Driver emotional states significantly influence road safety, yet comprehensive benchmarks for emotion-aware driving systems remain limited. This paper presents the first large-scale multimodal benchmark for simultaneous driver emotion recognition, behavior classification, and driving scene understanding using Vision-Language Models (VLMs). We introduce a dataset comprising 13,200 visual question-answering pairs and 2,200 dense captions, evaluating state-of-the-art VLMs across three dimensions: driver emotion recognition across six emotional states, behavior classification spanning eight types, and contextual scene understanding including weather, time of day, traffic density, and vehicle motion. Our evaluation of multiple VLMs reveals distinct performance patterns: while models demonstrate strong capabilities in identifying environmental context such as weather and time of day, they exhibit significant limitations in recognizing driver emotions and describing vehicle dynamics. These findings highlight fundamental challenges in applying current VLMs to human-centric and dynamics-aware driving scenarios. This benchmark establishes a standardized evaluation framework for emotion-aware advanced driver assistance systems and provides insights into multimodal reasoning capabilities required for comprehensive driver state monitoring.