A data-driven behavior generation algorithm in car-following scenarios
The conventional Adaptive Cruise Control system lacks user-friendly design. In this paper, a novel method for learning a generative model from human drivers’ car-following data using an automaton learning algorithms is proposed. By partitioning the model using frequent common state sequences, human driving patterns are extracted and clustered. Then a cluster identification method is used to obtain the current driving pattern and generate a desired acceleration. The experiments validate that the simulated trajectories of the proposed method are more similarly to human drivers than those of conventional PID controller.