EEPC: A Framework for Energy-Efficient Parallel Control of Connected Cars

摘要

With the advanced communication sensors are deployed into the modern connected vehicles (CVs), large amounts of traffic information can be collected in real-time, which gives the chance to explore the various techniques to control the routing of CVs in a ground traffic network. However, the control of CVs often suffers from energy inefficiency due to the constant changes of network capacity and traffic demand. In this paper, we propose a cost-based iterative framework, named EEPC, to explore the energy-efficient parallel control of connected vehicles. EEPC enables the control of CVs to iteratively generate a feasible solution, where the control of each vehicle is guided in an energy-efficient way routing on its own trajectory. EEPC eliminates the conflicts between CVs with a limited number of iterations and in each iteration, EEPC enables each vehicle to coordinate with other vehicles for a same road resource of the traffic network, further determining which vehicle needs the resource most. Note that at each iteration, the imposed cost is updated to guide the coordination between CVs while the energy is always used to guide the control of CVs in EEPC. In addition, we also explore the parallel control of CVs to improve the real-time performance of EEPC. We provide two parallel approaches, one is fine grain and the other is coarse grain. The fine grain performs the parallel control of single-vehicle routing while the coarse grain performs the parallel control of multi-vehicle routing. Note that fine grain adopts multi-threading techniques and coarse grain adopts MPI techniques. The simulation results show that the proposed EEPC can generate a feasible control solution. Notably, we also demonstrate that the generated solution is effective in eliminating the resource conflicts between CVs and in suggesting an energy-efficient route to each vehicle. To the best of our knowledge, this is the first work to explore energy-efficient parallel control of CVs.