NONE 20260624113105806 wseas/wseas content-registration-form@crossref.org content-registration-form DESIGN CONSTRUCTION MAINTENANCE 2944-912X 2732-9984 Salvatore Golfo Engineering Department University of Palermo Viale delle Scienze, Ed. 8 ITALY Alessandro Muratore Engineering Department University of Palermo Viale delle Scienze, Ed. 8 ITALY Filippo Carollo Engineering Department University of Palermo Viale delle Scienze, Ed. 8 ITALY This review article analyzes the evolution of numerical simulation methodologies and the biomechanical evaluation of damage in vehicle-pedestrian collisions to enhance the protection of vulnerable road users. The work explores the transition from multibody systems (MBS) to high-fidelity finite element (FE) models and mathematical surrogate frameworks, highlighting the emerging role of Artificial Intelligence and Deep Reinforcement Learning in modeling dynamic interactions and complex behaviors. It critically examines key injury parameters (HIC, Nij, TTI) and the influence of demographic variables such as age and body mass, integrating the "age-infusion" approach and the study of models for obese populations to personalize injury risk curves. The article delves into the phenomenology of impact, distinguishing between primary and secondary (ground) impacts and analyzing how vehicle geometry—from SUV profiles to the inclined design of trams—influences kinematic trajectories. Furthermore, it quantifies the non-linear correlation between impact speed and the probability of death, providing biomechanical support for the implementation of new urban speed limits. Finally, future challenges are discussed, such as the integration of V2X communication systems and the need to correlate vehicular structural failure with human body tolerance to optimize energy-absorbing crumple zones. 06 24 2026 06 24 2026 34 3 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232022.2026.6.3 https://wseas.com/journals/dcm/2026/a06dcm-003(2026).pdf Zhao, R, Rahman, A, Wei, B, Li, C, Ma, Y, Cai, Y & Han, L 2024, A literature review of contacting force measurement methods for pedestrian crowds, Heliyon, vol. 10, no. 21, p. e39755. Frantisek Lopot, Lubos Tomsovsky, Marsik, F, Masek, J, Petr Kubovy, Jezdik, R, Sorfova, M, Barbora Hajkova, Dita Hylmarova, Havlicek, M, Ondrej Stocek, Doubek, M, Tikkanen, T, Svoboda, M & Jelen, K 2023, Pedestrian Safety in Frontal Tram Collision, Part 1: Historical Overview and Experimental-Data-Based Biomechanical Study of Head Clashing in Frontal and Side Impacts, Sensors, vol. 23, Multidisciplinary Digital Publishing Institute, no. 21, pp. 8819–8819, viewed 25 October 2024, <https://www.mdpi.com/1424- 8220/23/21/8819>. Nellippallil, AB, Berthelson, PR, Peterson, L & Prabhu, RK 2022, A Computational Framework for Human-Centric Vehicular Crashworthiness Design and DecisionMaking Under Uncertainty, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, vol. 8, no. 2. Alsharif, T, Lanzaro, G & Sayed, T 2024, Distracted Walking: Does it impact pedestrian-vehicle interaction behavior? Accident Analysis & Prevention, vol. 208, Pergamon, p. 107789. Ren, AD, Zhang, BG, Chang, CF & Huang, DH 2025, Two-step deep reinforcement learning for traffic signal control to improve pedestrian safety using connected vehicle data, Accident Analysis & Prevention, vol. 222, Elsevier BV, pp. 108161–108161. Hu, J, Lin, Y-S, Boyle, K, Bonifas, A, Lin, C-H, Tatem, W & Martin, P 2025, Predicting head impact conditions in vehicle-to pedestrian impacts through computational human modeling, Traffic Injury Prevention, vol. 26, no. sup1. Kohles, SS & McClaren, JW 2022, A stochastic model validated with human test data causally associating target vehicle Delta V, occupant cervicocranial biomechanics, and injury during rear-impact crashes, Journal of Forensic and Legal Medicine, vol. 91, Elsevier BV, pp. 102431–102431. Hussain, Q, Feng, H, Grzebieta, R, Brijs, T & Olivier, J 2019, The relationship between impact speed and the probability of pedestrian fatality during a vehiclepedestrian crash: A systematic review and meta-analysis, Accident Analysis & Prevention, vol. 129, pp. 241–249. Prasad, P, Mertz, H, Augenstein, J & Digges, K 2010, 0S-44 Evaluation of the Field Relevance of Several Injury Risk Functions, Stapp Car Crash Journal, vol. 54. Golfo, S 2021, Analisi della gravità delle lesioni nellimpatto veicolo-pedone. Mariotti Virzì, G 2009, MULTIBODY NUMERICAL SIMULATION FOR VEHICLE – PEDESTRIAN CRASH TEST, vol. 62, pp. 40–49. Shi, L, Han, Y, Huang, H, Li, Q, Wang, B & Mizuno, K 2018, Analysis of pedestrianto-ground impact injury risk in vehicle-topedestrian collisions based on rotation angles, Journal of Safety Research, vol. 64, pp. 37–47, viewed 6 December 2019, <https://www.sciencedirect.com/science/arti cle/pii/S0022437517303705>. Berthelson, PR, Ghassemi, P, Wood, JW, Liu, Y, Al-Graitti, AJ, Jones, MD, Chowdhury, S & Prabhu, RK 2021, Evaluation of occupant neck injury response to varied impact conditions using a finite element-mathematical surrogate modeling approach, International Journal of Crashworthiness, vol. 27, no. 6, pp. 1726– 1742. Nusia, J, Xu, J-C, Knälmann, J, Sjöblom, R & Kleiven, S 2023, Injury risk functions for the four primary knee ligaments, Frontiers in Bioengineering and Biotechnology, vol. 11, p. 1228922, viewed 27 November 2023, <https://pubmed.ncbi.nlm.nih.gov/37860626/ >. Li, C, Liu, S & Cen, X 2021, Safety and efficiency impact of pedestrian–vehicle conflicts at non signalized midblock crosswalks based on fuzzy cellular automata, Physica A Statistical Mechanics and its Applications, vol. 572, Elsevier BV, pp. 125871–125871. Lee, MB, Lee, CT, Abas, MA & Chong, WWF 2025, Advancing pedestrian safety in the era of autonomous vehicles: A bibliometric analysis and pathway to effective regulations, Journal of Traffic and Transportation Engineering (English Edition), Elsevier. J.A., P 2012, Development Process of Injury Risk Curves, viewed 28 December 2025, <https://www.researchgate.net/publication/2 87059557_Development_process_of_injury_ risk_curves>. Mariotti Virzì, G 2019, Head Injury Criterion: Mini Review, American Journal of Biomedical Science & Research, vol. 5, no. 5, pp. 406–407. Narayan Yoganandan, Banerjee, A & Pintar, FA 2015, Age-Infusion Approach to Derive Injury Risk Curves for Dummies from Human Cadaver Tests, Frontiers in Bioengineering and Biotechnology, vol. 3, Frontiers Media, viewed 19 May 2025, <https://pmc.ncbi.nlm.nih.gov/articles/PMC4 677537/>. Virzi Mariotti, G, Golfo, S, Carollo, F & Scalici, E 2021, Study of an impact vehicle-bike at high speed, Academia Letters. Adnan Yusuf, S, Khan, A & Souissi, R 2024, Vehicle-to-everything (V2X) in the autonomous vehicles domain – A technical review of communication, sensor, and AI technologies for road user safety, Transportation Research Interdisciplinary Perspectives, vol. 23, no. 2590-1982, p. 100980. Unar, S, Nazar, M, Khokhar, S, Ahmed, A, Su, Y & Elkhalil, A 2026, Artificial Intelligence-driven visual attention framework for vehicle–pedestrian interaction in Intelligent Transportation System, Engineering Applications of Artificial Intelligence, vol. 165, p. 113455.