Abstract:

Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase throughput, and optimize energy efficiency and emissions in complicated traffic scenarios. This paper presents a mixed-integer linear programming (MILP) method for scheduling and coordination of CAVs in a highly dynamic environment that consists of multiple human-driven vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a centralized high-level decision making problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. The performance and computational load of the proposed method are assessed in closed-loop simulations on an illustrative scenario. Despite MILP having combinatorial complexity, the proposed formulation appears feasible for realtime implementation, e.g., in mobile edge computers (MECs).