Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment
This addresses decision-making for automated vehicles in transportation systems, representing an incremental improvement over existing methods.
The paper tackles automated vehicle decision-making by proposing a framework combining deep reinforcement learning with high-fidelity simulation, achieving a 7.9% efficiency increase in car-following and a further 2.4% speed gain in integrated lane-changing scenarios compared to classical models.
Automated vehicles are deemed to be the key element for the intelligent transportation system in the future. Many studies have been made to improve the Automated vehicles' ability of environment recognition and vehicle control, while the attention paid to decision making is not enough though the decision algorithms so far are very preliminary. Therefore, a framework of the decision-making training and learning is put forward in this paper. It consists of two parts: the deep reinforcement learning training program and the high-fidelity virtual simulation environment. Then the basic microscopic behavior, car-following, is trained within this framework. In addition, theoretical analysis and experiments were conducted on setting reward function for accelerating training using deep reinforcement learning. The results show that on the premise of driving comfort, the efficiency of the trained Automated vehicle increases 7.9% compared to the classical traffic model, intelligent driver model. Later on, on a more complex three-lane section, we trained the integrated model combines both car-following and lane-changing behavior, the average speed further grows 2.4%. It indicates that our framework is effective for Automated vehicle's decision-making learning.