Autonomous Vehicle Forensics: Investigating Data Streams for Traffic Prediction and Incident Mitigation

Abstract

The growing implementation of autonomous cars in intelligent transportation systems requires solid traffic forecasting and incident prevention mechanisms. Yet, there are difficulties in attaining system interoperability and user acceptability. In this research, a deep learning-based framework is suggested for traffic forecasting and prevention based on the use of a forensic method on autonomous car data. A restricted boltzmann machine derives deep, weighted features which are subsequently handled by an adaptive dilated long short-term memory model optimized by using the position updated osprey optimization algorithm. Forecasted traffic data are analyzed further to formulate mitigation strategies such as optimized path planning. Experimental results demonstrate better performance compared to the baseline methods based on various metrics, highlighting the effectiveness of the framework in improving future transportation systems and autonomous vehicle forensics.