Decentralized Human-Like Ramp Merging Decision-Making and Control Based on a Stochastic Potential Game

Freeway ramp merging control in the mixed traffic consisting of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) is one of the bottlenecks in the development of autonomous driving (AD) technologies due to complex multi-vehicle interactions. To address this, we propose a decentralized human-like control framework to help CAVs merge smoothly and interact more effectively with HDVs in a mixed-traffic environment. First, a stochastic potential game model is proposed to characterize the uncertainty of HDVs’ actions and optimize individual actions while considering their impact on the global system. A model predictive controller (MPC) is designed to minimize the accumulated cost of the potential function over a time horizon. Next, a distributed algorithm is developed to solve the proposed optimization problem in parallel while reducing the computational burden. To capture the characteristics of human driving behavior and help CAVs take more human-like actions, we calibrate the parameters in the proposed model using a real-world trajectory dataset. The performance of the proposed method is tested in a realistic two-lane merging zone scenario. Experimental results show that the proposed method enables CAVs to merge smoothly and ultimately improves traffic efficiency in merging zones. Additionally, the solution quickly converges to the optimal result using the proposed distributed algorithm, supporting its application in decentralized AD systems.