MIT Researchers Reveal Optimized Mathematical Model for Driverless Platooning

The model focuses on two components, i.e., a formula for arrival times of the trucks and a formula to monitor fuel consumption of the platoon.

Michael Cheng
    Dec 28, 2016 2:25 PM  PT
MIT Researchers Reveal Optimized Mathematical Model for Driverless Platooning
author: Michael Cheng   

In commercial and industrial sectors, driverless technology can be applied to reduce fuel consumption by up to seven percent during deliveries or trucking operations. Autonomous platooning, or a string of massive vessels moving simultaneously while communicating with each other, is the system that many businesses are interested in leveraging.

With strong reliance on long-distance highways, this technology could come to fruition before self-driving cars hit mainstream markets. Key players in the field currently pushing platooning to its limits include Uber (via acquired startup, Otto), the US Army and the American Trucking Association (ATA). Regulators, such as Michigan Gov. Rick Snyder, are taking a progressive stance in the application of platooning by allowing businesses to utilize the technology on public roads.

But what exactly is driverless platooning? And how will it disrupt traditional logistics, commercial trucking and large-scale distribution?

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Optimized Fuel Consumption and Productivity

The iconic formation of platooning offers less aerodynamic drag due to tight spacing between each vessel. An analysis from MIT during the International Workshop on the Algorithmic Foundations of Robotics revealed that during long-distance deliveries, trucks consume fuel to overcome aerial drag surrounding the vessel. By limiting the space by just a few meters, the units in the middle of the platoon could experience less drag. As a result, fuel consumption for those vehicles is reduced by up to 20 percent, while the last truck on the string could save up to 15 percent of fuel. Theoretically, the more vehicles participating in the platoon, the greater the savings for businesses.

"Truck platooning relies on sensors and vehicle-to-vehicle communications, known as V2V, between the connected trucks. For example, if the lead vehicle brakes, the follow vehicle's brakes may be automatically activated," said Kara Kapke from Barnes & Thornburg LLP.

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Mathematical Model for Scheduling 

A major drawback with the nascent technology is scheduling and delays. This is because in order for platooning to be effective, a handful of trucks must leave the location together. If a vehicle has to wait several hours for other vessels at the loading bay, it would be considered unproductive, resulting in under-utilized trucks. To solve this issue, a team of MIT scientists developed a mathematical model for scheduling policies.

The model focuses on two components, i.e., a formula for arrival times of the trucks and a formula to monitor fuel consumption of the platoon. Researchers applied this model to various scheduling processes, including a time-table policy and a feedback policy. After conducting several tests, the group uncovered that simple scheduling frameworks, like time-table policies, are most sustainable for companies that incorporate commercial platooning in their business.

"That is, 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," explained Jennifer Chu from MIT.

By comparison, feedback scheduling only offers up to five percent savings in fuel consumption.

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