A Study on Autonomous Intersection Management: Planning-Based Strategy Improved by Convolutional Neural Network

The development and application of autonomous vehicles bring great changes to urban traffic management and control. As one of the bottlenecks to improve transportation efficiency, intersection management plays an important role in the urban city. When the dynamic control method in different cases is determined, the key of autonomous intersection management problem is to search the passing orders for approaching connected and automated vehicles (CAVs). The paper proposed a framework based on convolutional neural network to predict different passing orders’ total time consumption. Thus, the best passing order with the lowest time consume can be chosen as the optimal solution. Then continuous-time optimal control can be carried out on CAVs. Meanwhile, sequential model-based algorithm configuration technique is used for neural network training. Simulation results exported from Simulation of Urban Mobility (SUMO) indicate that the proposed method outperforms actuated signal control and first come first serve strategy. The average delay of the proposed method can decrease by 42.40%–73.05% compared with actuated signal control and 2.95%–55.29% compared to first come first serve strategy. Moreover, it can increase average speed by more than 20% compared with the other two methods. The proposed method can significantly reduce the computation time comparing with the original planning-based strategy. At last, the framework can be applied to other regression tasks like vehicle emissions, then different optimization targets can be estimated to get better solutions faster.