Model-Less Location-Based Vehicle Behavior Prediction for Intelligent Vehicle

Predicting surrounding vehicle behavior plays an important role in an intelligent vehicle. Optimization of control strategy considering predicted future events could provide significant benefits by improving efficiency, comfort, and safety. However, realizing such prediction in an arbitrary environment is a challenging task as the real environment is highly diverse. In this paper, we propose a model-less location-based prediction method for a connected vehicle, which shares driving data through a cloud server. The shared data are stored in a relational database management system after associated with the location information. Surrounding vehicle behavior is then predicted with kernel density estimation by referring to nearby data, which implicitly reflect all location-dependent factors, such as road design, traffic rule, and region. Since this method does not rely on any pre-trained models, prediction performance is not affected by the overfitting issue. The performance of the proposed method has been evaluated by applying to optimization-based adaptive cruise control, which minimizes energy loss and a following error based on predicted future position of a preceding vehicle. The experimental result with urban driving data shows that the proposed method is more accurate and fuel efficient than several baseline models including kinematic model and neural networks.