On-ramp merging strategy for connected and automated vehicles based on complete information static game

Abstract

Improper handling of vehicle on-ramp merging may hinder traffic flow and contribute to lower fuel economy, while also increasing the risk of collisions. Cooperative control for connected and automated vehicles (CAVs) has the potential to significantly reduce negative environmental impact while also improve driving safety and traffic efficiency. Therefore, in this paper, we focus on the scenario of CAVs on-ramp merging and propose a centralized control method. Merging sequence (MS) allocation and motion planning are two key issues in this process. To deal with these problems, we first propose an MS allocation method based on a complete information static game whereby the mixed-strategy Nash equilibrium is calculated for an individual vehicle to select its strategy. The on-ramp merging problem is then formulated as a bi-objective (total fuel consumption and total travel time) optimization problem, to which optimal control based on Pontryagin's minimum principle (PMP) is applied to solve the motion planning issue. To determine the proper parameters in the bi-objective optimization problem, a varying-scale grid search method is proposed to explore possible solutions at different scales. In this method, an improved quicksort algorithm is designed to search for the Pareto front, and the (approximately) unbiased Pareto solution for the bi-objective optimization problem is finally determined as the optimal solution. The proposed on-ramp merging strategy is validated via numerical simulation, and comparison with other strategies demonstrates its effectiveness in terms of fuel economy and traffic efficiency.

Keywords

• Connected and automated vehicles
• On-ramp merging
• Complete information static game
• Optimal control
• Varying-scale grid search