The traditional accident liability assessed mainly by manual video reviewing and subjective human judgment is inefficient and leading to inefficiencies, inconsistencies, and frequent disputes. e absence of automation also makes claims less efficient and transparent.
An AI system was build to process accidents data (video and images + audio inputs) in order to, impartially, calculate the percentage of the responsibility that each party has. Developed with Python, Streamlit and AI models such as K-Means Clustering and Anomaly Detection, the solution enables quicker, data-based decision making.
Discover how AI is changing the game with liability determination in accident reconstruction.