Hierarchical Optimal Maneuver Planning and Trajectory Control at On-Ramps With Multiple Mainstream Lanes

Connected Automated Vehicles (CAVs) have the potential to improve traffic operations when they cooperatively maneuver in merging sections. State-of-the-art approaches cooperative either build on heuristics solutions or prohibit mainline CAVs change lane multilane highways. This paper proposes a hierarchical control approach that ensures collision-free and traffic-efficient through interaction of planner an operational trajectory controller. The predicts future vehicular trajectories, including acceleration trajectories time instants changes start, long horizon up 50 seconds with linear prediction model. It establishes optimal dynamic vehicle sequence each by minimizing predicted disturbances can propagate upstream lead breakdown. During process, vehicles may facilitate on-ramp merging, albeit higher ego cost. controller follows established instructions from regulates model predictive shorter 6 seconds. performance designed was compared method utilizing widely used first-in-first-out rule establish sequences same generate trajectories. Systematic comparison shows proposed consistently results less during under 528 different scenarios states, initial desired gap settings. On average, decrease 39.18% observed.