Integrating Machine Learning in Pedestrian Forensics: A Comprehensive Tool for Analysing Pedestrian Collisions

Analysis of pedestrian-to-vehicle collisions can be complex due to the nature of the interaction and the physics involved. The scarcity of evidence like video evidence (from CCTV or dashcams), data from the vehicle's ECU, witness accounts, and physical evidence such as tyre marks, complicates the analysis of these incidents. In cases with limited evidence, current forensic methods often rely on prolonged inquiry processes or computationally intensive simulations. Without adequate data, accurately estimating pedestrian kinematics and addressing hit-and-run scenarios becomes challenging. This research provides an alternative approach to enhancing pedestrian forensic analysis based on machine learning (ML) algorithms trained on over 3000 multi-body computer simulations with a diverse set of vehicle profiles and pedestrian anthropometries. Leveraging information such as vehicle profile, damage, and pedestrian attributes like height and weight, the ML algorithm estimates essential parameters like vehicle impact speed, pedestrian gait, crossing speed, and crossing direction. The proposed ML algorithm was evaluated against real-world data from the UK Road Accident In Depth Studies (RAIDS) and proved to
be accurate in predicting impact conditions within an error tolerance of 10%. This ML-based technology provides forensic investigators with vital pedestrian collision parameters early in the inquiry, enabling a focused analysis on a reduced collision parameter set. First responders can swiftly estimate speed characteristics, and forensic analysts can streamline their investigations, potentially aiding legal procedures and enhancing post-impact care through the use of this in-situ tool.