Driverless trucks, supply chains, AI and Big Data – How does it work?

Published October 17, 2018   |   

You would’ve heard of driverless cars. The technology would soon be redefining the way people commute, not only around the world but between and in cities as well. Driverless technology is not only being applied to personal vehicles, but also to commercial transportation. Driverless trucks are the natural evolution of driverless cars. They will cut down on costs, increase efficiency, and generally shake up the shipping industry. Let’s look at how artificial intelligence technology is changing the outlook of the freight industry.


However, at the moment, “driverless trucks” will be a misnomer. There will still be someone in the cab, monitoring the truck like a conductor, rather than actively driving. Much like experimental driverless taxis or the driverless Google cars, there’s someone behind the wheel in case something goes wrong.

In the case of shipping, however, it’s also to make sure the truck is on schedule, making the correct deliveries, and more. It’s not something a driver would have to constantly do, but rather they must check in every so often to make sure all systems are running properly. The driver is a failsafe more than anything, and perhaps a person to provide a signature upon delivery.

The future might prove the moniker correct, however, in that a driver might instead sit at a control panel, monitoring a few trucks at the same time from a remote location. It will be similar to the military and their drone program, where the pilots can be hundreds of miles away.

Supply Chain and LTL

This will put more pressure on the supply chain itself. Because the name of the game is increasing efficiency. And when a computer is doing most of the work on the shipping end, it’s up to the director of supply chain management to choose the best routes. This means efficiency in what items are assigned to each truck, the fastest route for that truck to hit all the stops, and more.

However, such a process would require implementing the less than truckload (LTL) methodology. The concept essentially focuses speed rather than quantity. In order to increase stability and speed, a truck is not filled completely. While this may indicate an inefficient use of truck space, it also makes deliveries safer and faster.

Big Data

This is where big data comes in. Crunching data can help choose the optimal inventory per truck based on a route. Just like solving a game of Tetris. By automating the planning and scheduling of a truck’s inventory, you optimize the amount of cargo that goes in which truck. On top of that, big data can be used to identify routes and set timelines. Because driverless cars do not need to work on a human schedule and thereby never get tired, the actual drive for deliveries can be scheduled at 3 a.m. It would be the ideal time for the driverless truck to arrive in a city and avoid any traffic, as well as minimize the chances of accidents.

As it stands, drivers can only spend 11 hours on the road in a 24-hour period, and only 7.5 to 8 of those hours are actually spent driving. The rest is waiting. Big data is already making drivers more efficient by collecting data from IoT devices, and miles driven needs to be counted for IRS purposes, but much of this will be rendered obsolete without the need for an actual driver.


One consequence of implementing driverless trucks would be a massive displacement of human drivers. However, their experience on the road and acquired knowledge of the mechanisms of trucks can be put to use. There are companies in the transportation industry that would still require these drivers’ skills. US Xpress, for example, collects data on fuel usage and tire conditions. While fuel usage will still an important discussion and can also help plan more efficient routes, tracking tire conditions brings up the question of maintenance. A driverless car could, in theory, run 24 hours a day. This, however, will wear down parts and could end up driving costs higher to keep the truck in working condition. Big data can help identify when trucks will need maintenance and where the best place to get the right part is.

Big data will be vital for driverless cars. It will inform everything from inventory and route to optimal traffic times and maintenance tracking. The industry is still a way off of completely replacing drivers, but it’s on the horizon — an efficient, cost-effective, data-driven horizon.