AI in Freight and Trucking

Freight is a market of shippers with loads and carriers with trucks. Brokers have traditionally connected the two by phone. Digital freight platforms such as Uber Freight apply machine learning to this matching. They predict which carriers are likely to accept a load, recommend loads that fit a driver's location and remaining hours, and try to reduce deadhead, the miles trucks drive empty. Convoy, a prominent venture-backed digital broker, shut down in 2023 during a freight downturn. It is a reminder that better matching can't overcome a weak market on its own. Pricing is a forecasting problem. Spot rates (prices for one-off loads) vary by route, day, season, fuel price and the balance of trucks to loads. Models trained on past transactions and market indices produce instant quotes, but prices swing enough that brokers still manage risk and override quotes. Safety cameras are among the most widespread uses of AI in trucking today. Systems from companies such as Samsara, Motive and Lytx use road-facing and driver-facing cameras with computer vision. They detect distraction, signs of drowsiness, phone use, rolling stops and tailgating. They alert drivers in real time and send clips to safety managers. Fleets also use the video to clear drivers of blame after crashes. Drivers have raised privacy concerns about constant recording inside the cab. Autonomous trucking has had a rocky path. Several companies, including Embark and TuSimple's US operation, exited around 2023, and Waymo paused its trucking work. Aurora began commercial driverless operations on a Dallas-Houston route in 2025. Others, such as Kodiak, have run driverless operations in more limited settings. The dominant model is hub to hub: autonomous trucks drive the highways between transfer yards, and human drivers take the complex local routes. A common misconception is that autonomy will soon replace drivers everywhere. Current deployments cover specific routes, conditions and regulatory settings.