PRIORITIZED COLLISION RISK ASSESSMENT FOR AUTONOMOUS VEHICLES: ENHANCING PEDESTRIAN SAFETY

Abstract

For the development of autonomous vehicles (AVs) in city areas, the safety of pedestrians continues to be very important. This report introduces an innovative algorithm for assessing collision risk with multiple pedestrians. The main feature of this algorithm is its ability to rank collision risks according to different pedestrian traits. By using predictive analysis and strategies for risk prioritization, this method can find and lessen possible collision situations before they occur. It greatly improves the safety level towards pedestrians within urban traffic settings The experimental validation shows that the algorithm for assessing risk is very effective in reducing collision rates among different groups of pedestrians. This important result helps to make AVs safer by dealing with potential collisions beforehand, giving more trust and security to people walking nearby. This paper has made a useful contribution to improve safety in AVs by presenting a thorough system for evaluating the possibility of pedestrian collisions. It sets a framework that can be used as standard way for future researches on this topic, promoting continuous development towards safer interactions between AVs and people walking on roads.