Human-driver speed profile modeling for autonomous vehicle's velocity strategy on curvy paths

As autonomous-vehicle-related technologies tend to be mature, improving passengers' experience by learning driving styles from human drivers becomes a promising research topic. This study aims at learning human drivers' velocity planning strategies for driving at curvy paths (e.g. negotiating sharp curves, turning at intersections, etc.) on structural road. First, we identified and extracted training trips from the latest naturalistic driving study database. Vehicle trajectories and the disturbances caused by other vehicles were estimated based on sensor data. Road characteristics, environmental parameters were identified from road information database and video clips. Then, neural network based models were developed to fit drivers' speed profiles under different driving situations. Five models with different prediction steps were trained by up to 600 driving trips. Three error criteria were used to evaluate the performance of proposed models. This study verified the possibility of using human drivers' experience to generate velocity recommendations for different driving conditions. The limitations of the models are also documented.