Optimal Control of Urban Intersection Scheduling for Connected Automated Vehicles

We propose a novel urban congestion-aware intersection scheduling model based on vehicle to infrastructure communication (V2I) for automated and connected vehicles. In this model, a combinational optimized model which combines passing order and vehicular motion control together is proposed. In order to resolve the intersection conflict issue and improve traffic capacity, driving tube and potential conflict matrix is applied in the schedule optimization model. Take the global average waiting time as optimized object, we propose state encoding approach to collect all the vehicle's information in the intersection. Then Deep Q Network (DQN) method is applied to resolve the scheduling problem, which outputs the driving tubes enable vector and subsequently 7th polynomial based motion planning trajectory planning is exploited to generate the most comfortable and most efficient trajectory for active vehicles. The optimal time cost profile will be feed back to intersection manager via V2I channel for next time scheduling decision. The performance of this framework is evaluated based on a typical Chinese complicated urban scenario with extensive simulation, our framework achieves encouraging results in terms of average waiting time and peak traffic throughput.