Evaluation of risk injury in pedestrians’ head and chest region during collision with an autonomous bus

Abstract

Autonomous vehicle (AV) can detect pedestrians with high accuracy by utilising various sensors. Although it is an undesirable condition, traffic crashes can occur if any sensor misfunctions. This paper focuses to measure the severity of pedestrian injury during autonomous bus-pedestrian crashes via finite element simulation as pedestrians are very vulnerable in traffic crashes. Two types of vehicle impact were considered (frontal and side), and risk injury in head and chest region were evaluated. Effect of vehicle velocity (ranging from 5–10 m/s) on risk probability were evaluated. It was found that higher velocity caused more damage to pedestrian body, and frontal impact was more severe than the side impact. Maximum values of head-injury-criteria and chest acceleration were 248 and 36 g, respectively, when vehicle speed was 10 m/s. AV developers can use these results to implement a safer AV design and reduce pedestrian risk injury.