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The accelerated deployment of Autonomous Vehicles (AVs) is fundamentally reshaping road traffic dynamics, establishing the framework for enhanced safety, operational efficiency, and sustainable mobility. Within this paradigm, the adaptation and optimization of traffic accident reconstruction and analysis tools are imperative to ensure an objective, rigorous, and bias-free situational assessment. The systematic analysis of road events involving AVs is a strategic priority, critical not only for the functional safety of traffic participants but also for fostering public trust in automated systems. The inherent fragility of this trust underscores the necessity for granular, technically grounded forensic explanations following any incident. This paper investigates the socio-technical impact of increasing AV penetration rates, focusing on public perception and the requirement for decisional transparency. Furthermore, the study evaluates how the integration of Artificial Intelligence (AI) algorithms and emerging Vehicle-to-Everything (V2X) communication architectures facilitates the development of models capable of high-fidelity environmental sensing and robust autonomous decision-making aimed at achieving full driving automation (SAE Level 4/5).