Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Traffic
This addresses the problem of autonomous driving decision-making in mixed traffic scenarios, but it appears incremental as it builds on existing deep reinforcement learning methods.
The paper tackles high-level driving behavior decision-making for connected vehicles in heterogeneous traffic by proposing a deep reinforcement learning model that learns optimal policies, such as driving fast without unnecessary lane changes, and demonstrates its capability through simulation.
High-level driving behavior decision-making is an open-challenging problem for connected vehicle technology, especially in heterogeneous traffic scenarios. In this paper, a deep reinforcement learning based high-level driving behavior decision-making approach is proposed for connected vehicle in heterogeneous traffic situations. The model is composed of three main parts: a data preprocessor that maps hybrid data into a data format called hyper-grid matrix, a two-stream deep neural network that extracts the hidden features, and a deep reinforcement learning network that learns the optimal policy. Moreover, a simulation environment, which includes different heterogeneous traffic scenarios, is built to train and test the proposed method. The results demonstrate that the model has the capability to learn the optimal high-level driving policy such as driving fast through heterogeneous traffic without unnecessary lane changes. Furthermore, two separate models are used to compare with the proposed model, and the performances are analyzed in detail.