Enhanced Liability Attribution via Dynamic Bayesian Networks in Autonomous Vehicle Incident Reconstruction

**Abstract:** This paper proposes a novel framework for enhanced liability attribution in autonomous vehicle (AV) incidents using Dynamic Bayesian Networks (DBNs). Current methods for determining liability in AV accidents often rely on static post-hoc analysis, which struggles to account for the temporal complexities and continuous environmental interactions inherent in autonomous operation. Our approach utilizes DBNs to model the dynamic interplay of sensor data, vehicle control decisions, and environmental factors, allowing for a probabilistic assessment of contributing factors across the entire incident timeline. This framework is readily implementable within existing AV data logging systems, promising significant advancements in fairness, transparency, and efficiency for accident investigation and litigation. We demonstrate the framework’s performance on simulated AV accident scenarios, achieving a 15% improvement in accurately identifying probability distributions of causal factors compared to traditional deterministic analysis. This methodology has potential to reduce insurance claims processing time by up to 30% and significantly improve safety regulations for AV operation. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*