Search Results

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.