Route Control Strategies for Autonomous Vehicles Exiting to Off-Ramps

This paper proposes three route control strategies for autonomous vehicles (AVs) exiting to off-ramps. First, two microscopic lane-changing models are developed to simulate the off-ramp behaviors for AVs. A risk factor A. is introduced in the discretionary lane-changing (DLC) model, and a five-step process is developed for the mandatory lane-changing (MLC) model. Second, three route control strategies are presented, i.e., grading strategy (GS), half-overlap strategy (HOS), and full-overlap strategy (FOS). Finally, the extensive numerical simulations are conducted to analyze the characteristics of each strategy based on the MATLAB platform. The results indicate that the studied area displays significant variability in spatiotemporal dynamics of average velocity and road capacity under different strategies. It is found that the GS is the mast effective control strategy for traffic congestion dissipation and travel delay reduction, followed by the HOS and, then, the FOS. The road capacity under the GS is increased by 19.1% and 7.8% compared to the FOS and the HOS, respectively. Furthermore, the GS consumes lower costs in terms of safety, travel efficiency, and route and lane switch frequency compared to the HOS and FOS, which is verified as the optimal strategy according to overall running cost. The findings of this paper can provide useful references for the design of AVs as well as the management of the automated highway systems.