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

Sainan Zhang, Jun Zhang, Weiguo Song, Tan Yue, Luyao Zhu

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 multisource aggregation model integrating physiological, behavioral, and emotional data, vehicle sensor inputs, and environmental conditions. ARISE employs multisource 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, a driving performance descriptor assesses driving workload and stability. Experiments show that ARISE significantly outperforms state-of-the-art methods in aggressive driving detection.

External IDs:doi:10.1109/mis.2025.3586243