Autonomous highway pilot using Bayesian networks and hidden Markov models

Abstract

In this paper we propose a data driven model for an autonomous highway pilot. The model is split into two basic parts, an acceleration/deceleration model and a lane change model. For modeling the acceleration, a Bayesian Network is used. For the lane change model, we apply a Hidden Markov Model. The lane change model delivers only discrete lane change events like stay on lane or change to left or right, but no exact trajectories. The model is trained with simulated traffic data, and validated in two different scenarios: in the first scenario, a single model controlled vehicle is embedded into a simulated highway scenario. In the second scenario, all vehicles on a highway are controlled by the model. The proposed model shows reasonable driving behavior in both test scenarios.