A Human Factors Engineering Approach to Feature Extraction and Safety Intervention in Intentional Vehicle-Pedestrian Collisions

Abstract

Intentional vehicle-pedestrian collisions exhibit fundamental distinctions from conventional traffic accidents, potentially constituting serious criminal offenses. However, judicial practice faces significant challenges in investigation, evidence collection, and case characterization. Grounded in human factors engineering theory, this study integrates multi-source data from 26 authentic judicial cases—including EDR data, surveillance footage, and on-site investigation reports—to construct a heterogeneous dataset encompassing vehicle operational status, driver behavioral characteristics, and environmental contextual information. Through high-fidelity accident reconstruction and vehicle condition restoration via the PC-Crash simulation platform, coupled with spatiotemporal sequence analysis employing dynamic time warping algorithms, this research systematically elucidates the dynamic correlation mechanisms between driver operations and pedestrian trajectories. A feature-weighted risk classification model was developed, with weight allocation meticulously considering statistical analysis of accident cases and human factors principles, thereby highlighting the relative importance and interactive relationships among vehicle status, driver behavior, and environmental context. By establishing a three-level safety intervention strategy, a paradigm shift from passive forensics to proactive prevention has been achieved. Experimental validation demonstrates that the proposed method attains 83.5% recognition accuracy on test sets while maintaining a false alarm rate below 6.8%, providing scientific evidence for characterizing intentional collision cases and establishing a theoretical foundation for intelligent connected vehicles' active safety design. The primary innovation lies in integrating human factors engineering theory with judicial practice requirements, constructing a comprehensive technical framework from feature extraction to safety intervention, thereby advancing traffic safety management from post-hoc analysis to preemptive prevention.

Keywords: Human Factors Engineering, Intentional Pedestrian Collision, Feature Extraction, Safety Intervention, Multi-source Data Fusion, Risk Assessment

DOI: 10.54941/ahfe1007880