Open Access

ARTICLE

Influence of Autonomous Vehicle Front-End Geometry on Pedestrian Injury Redistribution: A Multibody Simulation Study

Adrian Soica, Bogdan Cornel Benea^*

Department of Automotive and Transport Engineering, Transilvania University of Brasov, Brasov, Romania

* Corresponding Author: Bogdan Cornel Benea. Email:

Computer Modeling in Engineering & Sciences 2026, 147(3), 34 https://doi.org/10.32604/cmes.2026.082801

Received 23 March 2026; Accepted 27 May 2026; Issue published 30 June 2026

Abstract

This study investigates the influence of autonomous vehicle (AV) front-end geometry on pedestrian injury biomechanics using PC-Crash multibody simulations. While emerging vehicles promise improved urban safety through automation and collision avoidance technologies, their unconventional front-end architectures introduce new passive safety challenges. The research compares classical passenger vehicles with van-type and symmetric flat-front autonomous platforms under standardized impact conditions at 40 km/h. Results reveal a clear redistribution of injury mechanisms depending on vehicle geometry. Conventional sloped front-end vehicles, super-mini and compact class, generate higher Head Injury Criterion (HIC) values due to wrap-around kinematics, where pedestrians rotate onto the hood and windshield. In contrast, vertically oriented and flat-front autonomous designs significantly reduce HIC values by suppressing rotational motion. However, this reduction in head injury risk is accompanied by substantially increased thoracic peak forces, particularly in ROBO1-type configurations, where chest loads exceeded 15 kN. The findings demonstrate that lower HIC values do not necessarily indicate improved overall pedestrian safety. Instead, emerging autonomous geometries shift injury risk from the head to the thorax and lower limbs. The study highlights the need for balanced multi-criteria pedestrian assessment frameworks that incorporate thoracic injury metrics, geometric optimization, and stiffness distribution considerations in early-stage autonomous vehicle design.

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