A pilot study for investigating the feasibility of supervised machine learning approaches for the classification of pedestrians struck by vehicles

This research focuses on the application of Artificial Intelligence (AI) methodologies to the problem of classifying vehicles involved in lethal pedestrian collisions. Specifically, the vehicle type is predicted on the basis of traumatic injury suffered by casualties, exploiting machine learning algorithms. In the present study, AI-assisted diagnosis was shown to have correct prediction about 70% of the time. In pedestrians struck by trucks, more severe injuries were appreciated in the facial skeleton, lungs, major airways, liver, and spleen as well as in the sternum/clavicle/rib complex, whereas the lower extremities were more affected by fractures in pedestrians struck by cars. Although the distinction of the striking vehicle should develop beyond autopsy evidence alone, the presented approach which is novel in the realm of forensic science, is shown to be effective in building automated decision support systems. Outcomes from this system can provide valuable information after the execution of autoptic examinations supporting the forensic investigation. Preliminary results from the application of machine learning algorithms with real-world datasets seem to highlight the efficacy of the proposed approach, which could be used for further studies concerning this topic.

Automated summary

This research focuses on using Artificial Intelligence methods to classify vehicles involved in fatal pedestrian collisions, predicting vehicle types based on the traumatic injuries suffered by casualties, with a correct prediction rate of around 70%. Trucks causing collisions result in more severe injuries, while cars cause more lower extremity fractures in pedestrians. Although further evidence is needed to differentiate the striking vehicles, this novel approach has shown effectiveness in building automated decision support systems.