MIT engineers have studied a simple vehicle-platooning scenario and determined the best ways to deploy vehicles in order to save fuel and minimize delays. Their analysis, presented this week at the International Workshop on the Algorithmic Foundations of Robotics (WAFR 2016), shows that relatively simple, straightforward schedules may be the optimal approach for saving fuel and minimizing delays for autonomous vehicle fleets. The findings may also apply to conventional long-distance trucking and even ride-sharing services.
Platooning for heavy-duty trucks is on the cusp of market introduction. Peloton plans to bring its first platooning system to market in 2017, and ARPA-E is funding research into platooning technology as part of its NEXTCAR project. (Earlier post.)
Rapid advances in autonomous-vehicle technology may soon allow vehicles to platoon on highways, leading to substantial fuel savings through reduced aerodynamic drag. While these aerodynamic effects have been widely studied, the systems aspects of platooning have received little attention. In this paper, we consider a class of problems, applicable to vehicle platooning and passenger ride-sharing, from the systems perspective.—Adler et al.
Ride-sharing and truck platooning, and even flocking birds and formation flight, are similar problems from a systems point of view. People who study these systems only look at efficiency metrics like delay and throughput. We look at those same metrics, versus sustainability such as cost, energy, and environmental impact. This line of research might really turn transportation on its head.—Sertac Karaman, co-author
Karaman and his colleagues developed a mathematical model to study the effects of different scheduling policies on fuel consumption and delays. They modeled a simple scenario in which multiple trucks travel between two stations, arriving at each station at random times. The model includes two main components: a formula to represent vehicle arrival times, and another to predict the energy consumption of a vehicle platoon.
The group looked at how arrival times and energy consumption changed under two general scheduling policies: a time-table policy, in which vehicles assemble and leave as a platoon at set times; and a feedback policy, in which vehicles assemble and leave as a platoon only when a certain number of vehicles are present.
In their modeling of vehicle platooning, the researchers analyzed different scenarios under the two main scheduling policies. For example, to evaluate the effects of time-table scheduling, they modeled scenarios in which platoons were sent out at regular intervals—for example, every five minutes—versus over more staggered intervals, such as every three and seven minutes. Under the feedback policy, they compared scenarios in which platoons were deployed once a certain number of trucks reached a station, versus sending three trucks out one time, then five trucks out the next time.
Ultimately, the team found the simplest policies incurred the least delays while saving the most fuel. Time tables set to deploy platoons at regular intervals were more sustainable and efficient than those that deployed at more staggered times. Similarly, feedback scenarios that waited for the same number of trucks before deploying every time were more optimal than those that varied the number of trucks in a platoon.
Overall, feedback policies were just slightly more sustainable than time-table policies, saving only 5% more fuel.
You’d think a more complicated scheme would save more energy and time. But we show in a formal proof that in the long run, it’s the simpler policies that help you.—Sertac Karaman
Karaman is currently working with trucking companies in Brazil that are interested in using the group’s model to determine how to deploy truck platoons to save fuel. He hopes to use data from these companies on when trucks enter highways to compute delay and energy tradeoffs with his mathematical model.
The researchers are also applying their simulations to autonomous ride-sharing services. Karaman envisions a system of driverless shuttles that transport passengers between stations, at rates and times that depend on the overall system’s energy capacity and schedule requirements. The team’s simulations could determine, for instance, the optimal number of passengers per shuttle in order to save fuel or prevent gridlock.
We believe that ultimately this thinking will allow us to build new transportation systems in which the cost of transportation will be reduced substantially.—Sertac Karaman
This research was funded, in part, by the National Science Foundation.
Aviv Adler, David Miculescu, and Sertac Karaman (2016) “Optimal Policies for Platooning and Ride Sharing in Autonomy-Enabled Transportation”