Enhancing Driving Behavior Analysis in Autonomous Systems: A Reservoir Computing and Temporal-Aware Machine Learning Approach

In the rapidly evolving domain of autonomous vehicles, ensuring safety and reliability through advanced anomaly detection is paramount. Reservoir Computing, a novel approach for processing time-series data in dynamic systems, stands out for its ability to capture complex temporal patterns efficiently. This paper introduces an innovative method that integrates Reservoir Computing with temporal-aware data analysis to enhance driver behavior assessment. Our approach employs a unique combination of autoencoder-based feature extraction and Reservoir Computing to analyze driving metrics from vehicle sensors. The autoencoder compresses and encodes these temporal features, which are then processed by a reservoir computing model, adept at processing intricate temporal dependencies in the data. To evaluate the effectiveness of our model we simulate with a GPS dataset of 10,000 taxis to identify various driving dynamics of speed, acceleration, and state changes providing a comprehensive view of driver behavior. We further categorize drivers into different sets based on their driving performance using a support vector machine (SVM) algorithm. Our algorithm marks a significant step forward in anomaly detection for autonomous vehicles, offering a route to safer driving experiences and advancing vehicle safety technologies.