Driverless cars that drive in large packs could save more time and fuel as these autonomous vehicles experience less aerodynamic drag when driving close together, according to a new MIT study. However, assembling a vehicle platoon to deliver packages between distribution centres, or to transport passengers between stations, requires time, researchers said.
The first vehicle to arrive at a station must wait for others to show up before they can all leave as a platoon, creating inevitable delays. Now, engineers at Massachusetts Institute of Technology (MIT) in the US have studied a simple vehicle-platooning scenario and determined the best ways to deploy vehicles in order to save fuel and minimise delays.
The analysis shows relatively simple, straightforward schedules may be the optimal approach for saving fuel and minimising delays for autonomous vehicle fleets.
The findings may also apply to conventional long-distance trucking and even ride-sharing services, researchers said.
“Ride-sharing and truck platooning, and even flocking birds and formation flight, are similar problems from a systems point of view,” said Sertac Karaman, Associate Professor of Aeronautics and Astronautics at MIT.
“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,” said Karaman.
Karaman said that for truck-driving – particularly over long distances – most of a truck’s fuel is spent on trying to overcome aerodynamic drag, that is, to push the truck through the surrounding air.
Scientists have previously calculated that if several trucks were to drive just a few meters apart, one behind the other, those in the middle should experience less drag, saving fuel by as much as 20 per cent, while the last truck should save 15 per cent – slightly less, due to air currents that drag behind.
If more vehicles are added to a platoon, more energy can collectively be saved. However, there is a cost in terms of the time it takes to assemble a platoon.
Karaman and his colleagues developed a mathematical model to study the effects of different scheduling policies on fuel consumption and delays. They modelled 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.