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As per WHO 2018 report, pedestrian fatalities account for 23% of world road accident fatalities. Every day 850 pedestrians lose their lives in the world. As per MoRTH 2018 report, 16% of road accident fatalities are of pedestrians in India. Everyday 64 pedestrians lose their lives in India. Based on accident data, one of the most common reason for the pedestrian fatality is head injury due to primary contact from vehicle front-end structure. Pedestrian head injury performance highly depends on front-end styling, bonnet stiffness, clearance with aggregates underneath the bonnet and hard contact points. During concept stage of vehicle development, safety recommendation on front-end design is provided based on geometric assessment of the class A surface.

The automotive industry has achieved remarkable advances in passenger car safety systems to mitigate the risk of injuries and fatalities caused by road accidents. However, to further improve vehicle safety, it is essential to have a deeper understanding of real-world accidents and the true safety benefits of various safety systems in the field. This requires a framework to evaluate the effectiveness of safety systems in reducing occupant injury and fatalities. This study aims to use machine-learning techniques to predict occupant injury severity by considering accident parameters and safety systems, using the Road Accident Sampling System - India (RASSI) real-world accident data. The RASSI database contains comprehensive accident data, including various factors that contribute to occupant injury. The study focused on fifteen accident parameters that represent key aspects of crash scenarios such as vehicle type, accident type, vehicle speed, and occupant details.