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High-level driving behavior decision-making is an open-challenging problemfor connected vehicle technology, especially in heterogeneous trafficscenarios. In this paper, a deep reinforcement learning based high-leveldriving behavior decision-making approach is proposed for connected vehicle inheterogeneous traffic situations. The model is composed of three main parts: adata preprocessor that maps hybrid data into a data format called hyper-gridmatrix, a two-stream deep neural network that extracts the hidden features, anda deep reinforcement learning network that learns the optimal policy. Moreover,a simulation environment, which includes different heterogeneous trafficscenarios, is built to train and test the proposed method. The resultsdemonstrate that the model has the capability to learn the optimal high-leveldriving policy such as driving fast through heterogeneous traffic withoutunnecessary lane changes. Furthermore, two separate models are used to comparewith the proposed model, and the performances are analyzed in detail.

Deep Reinforcement Learning Based High-level Driving Behavior Decision-making Model in Heterogeneous Traffic