External Driver Classification Using Reservoir Computing Enhancing Automated Vehicle Safety

Ensuring the safety of Autonomous Vehicles (AVs) in mixed traffic environments is a considerable challenge due to the unpredictable behaviors and diverse driving styles of human drivers. This paper introduces a novel framework for driver classification of the surrounding vehicles from the external viewpoint of AVs, utilizing Reservoir Computing (RC) and Transfer Learning (TL) with observable data such as speed, acceleration, and speed limits, preserving driver privacy. Privacy feature augmentation based on TL generates comprehensive characteristics from both source and target domains, while time-series data augmentation increases classification accuracy within a small-time window. Augmented feature metrics are processed by an RC-based classifier to predict driver characteristics. Performance analysis shows the F1-score can reach up to 0.997, and comparison studies confirm the framework achieves state-of-the-art performance. The experiments demonstrate the ability of the framework to enhance the accuracy and reliability of driver classification, improving the real-time adaptability of AVs in complex traffic scenarios.