Trajectory optimization of connected and autonomous vehicles at a multilane freeway merging area

Abstract

Merging areas on freeways are primary locations for bottlenecks due to vehicles’ mandatory lateral conflicts. These critical conflicts, however, are potentially avoidable with Connected and Autonomous Vehicle (CAV) technology. However, current literature using CAV technology mostly focuses on merging maneuvers between a single-lane mainline and an incoming ramp. An algorithm dealing with multilane merging areas is absent. In this paper, an online system control algorithm for multilane freeway merging areas is presented with a CAV environment based on optimizing vehicles’ lane changing and car following trajectories. First, the lane flow distribution is adjusted upstream of the merging point under a rule-based lane changing decision, which eventually balances downstream lane flow distribution. A Cooperative Lane Changing Control (CLCC) optimization model is proposed to ensure safe and smooth lane changing execution. Second, a Cooperative Merging Control (CMC) model is adopted, analyzed, and generalized to conduct merging maneuvers around the merging point. Third, a dynamic moving border point method is designed to coordinate the consecutive execution of the CLCC and CMC models. To validate the proposed algorithm, a simulation platform based on VISSIM is developed for online computation and visualization. A typical two-lane freeway merging area is studied. Results under various demand scenarios demonstrate that the proposed algorithm outperforms previous cooperative merging algorithms consistently with respect to delays and average travel speeds.