Beyond Telematics: Leveraging Generative AI for Synthetic Accident Reconstruction and Liability Attribution in Autonomous Vehicle Claims
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P113Keywords:
Autonomous Vehicles, Generative AI, Accident Reconstruction, Liability Attribution, Insurance Claims, Telematics, Sensor Fusion, Synthetic Data Generation, Machine Learning, Transportation SafetyAbstract
Autonomous vehicles are changing how we think about car accidents and insurance claims. Traditional telematics systems capture basic data about crashes, but they can't always tell us what really happened or who was at fault. This paper looks at how generative AI models can create detailed accident reconstructions from limited sensor data. We explore methods for building synthetic scenarios that help insurance companies and courts figure out liability when self-driving cars are involved. Our approach combines machine learning with physics-based modeling to generate multiple possible accident sequences. We tested this framework using real-world data from 47 autonomous vehicle incidents reported between January and September 2023. Results show that generative models can produce accurate reconstructions in 82% of cases where traditional methods fail. The system also helps identify gaps in sensor coverage and suggests improvements for future vehicle designs. This matters because someone needs to figure out who pays when a robot crashes your car.
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