ARISE: Explainable Multimodal Aggressive Driving Detection via Driver State and Environment Perception

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Abstract
Detecting aggressive driving is challenging but crucial for public safety. Existing methods rely on time-series data of drivers’ physiology, behavior, and vehicle movement but overlook driver’s emotion and environmental influences. We propose ARISE, a multi-source aggregation model integrating physiological, behavioral, and emotional data, vehicle sensor inputs, and environmental conditions. ARISE employs multi-source feature extraction, multimodal fusion, and a classifier to detect aggressive driving. Unlike graph-based methods that fails to detect gradual aggression shifts or transformer-based methods prone to delays, ARISE explicitly models vehicle state continuity and the aggressive driving environment. Motion similarity descriptor tracks state transitions, while aggression descriptor quantifies environmental aggression. Additionally, driving performance descriptor assesses driving workload and stability. Experiments show that ARISE significantly outperforms state-of-theart methods in aggressive driving detection.

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