Trajectory Planning for Connected and Automated Vehicles: Cruising, Lane Changing, and Platooning
This addresses efficiency and safety in transportation systems for CAVs, but is incremental as it builds on existing optimal control methods.
The paper tackled trajectory planning for connected and automated vehicles by developing an optimal control-based model that incorporates platooning and lane changing, and found through simulations that it improves fuel consumption and travel time in dynamic traffic streams, with potential second-hand benefits for upstream vehicles.
Autonomy and connectivity are considered among the most promising technologies to improve safety, mobility, fuel and time consumption in transportation systems. Some of the fuel efficiency benefits of connected and automated vehicles (CAVs) can be realized through platooning. A platoon is a virtual train of CAVs that travel together following the platoon head, with small gaps between them. Vehicles may also reduce travel time by lane changing. In this paper, we devise an optimal control-based trajectory planning model that can provide safe and efficient trajectories for the subject vehicle and can incorporate platooning and lane changing. We embed this trajectory planning model in a simulation framework to quantify its efficiency benefits as it relates to fuel consumption and travel time, in a dynamic traffic stream. Furthermore, we perform extensive numerical experiments to investigate whether, and the circumstances under which, the vehicles in upstream of the subject vehicle may also experience second-hand fuel efficiency benefits.