Improving Energy Efficiency and Mobility of Connected Fleets via Route Preview and Cooperative Control
|Principal Investigators:||Ardalan Vahidi, Clemson University, avahidi(at)clemson.edu|
|Student:||Nianfeng Wan, Clemson University|
|Government:||Paramsothy Jayakumar, U.S. Army TARDEC|
|Industry:||Chen Zhang, Ford Motor Company|
The main objective is to evaluate the impact wireless connectivity and cooperative control can have on efficiency and mobility of a fleet of military trucks. This is different from the well-explored technology of truck platooning when coordination is arranged centrally. Our proposed scenarios rely on recent advances in vehicular connectivity that enable individual vehicles to cooperate and exchange information within a fleet. This provides more autonomy to each individual vehicle while offering some of the benefits of cooperative motion. For instance in off-road scenarios with unknown terrain, a lead vehicle can communicate back to a backend server perceived soil conditions, its chosen path and its roll, yaw, and pitch. This generates a dynamic map of the unexplored terrain. Following vehicles can learn from the preceding vehicle’s "mistakes" and gradually reroute to faster, safer, or more energy efficient routes.
In the first year, we developed an off-road microsimulation environment with connected trucks, realistic road geometries, stringent terrain, and operation scenarios for cooperative cruise simulations. We also devised and implemented cooperative cruise algorithms that rely on i) partial information exchange between trucks and ii) road preview to harmonize motion and reduce idling intervals.
The goals of the proposed research in the 2nd year are 1) to gain in-depth understanding of how soldiers actually drive/operate their vehicle in convoys, 2) to mathematically formulate objective functions of interest to Army in off-road driving on a 2 dimensional plane, 3) to solve the posed optimization problems in real-time by taking advantage of shared intelligence of a connected fleet, 4) to quantify possible gains in the fleet performance (agility, safety, travel time, and energy efficiency) in the simulation environment.
- Nianfeng Wan, Ardalan Vahidi, and Andre Luckow, "Optimal Speed Advisory For Connected Vehicles in Arterial Roads and The Impact on Mixed Traffic," in press, Transportation Research, Part C, 2016. doi:10.1016/j.trc.2016.01.011
- Nianfeng Wan, Ardalan Vahidi, and Andre Luckow, "Reconstructing Maximum Likelihood Trajectory of Probe Vehicles Between Sparse Updates," Transportation Research, Part C, Vol. 65, pp. 16–30, April 2016. doi:10.1016/j.trc.2016.01.010