In a road race, a group of cyclists often follow one another in a line to reduce the aerodynamic drag and improve energy efficiency. This technique, known as platooning, has been used for decades in the sport of cycling. Today, the time-tested technique is making a huge footprint in the innovative transportation industry with the rise of automated heavy-truck platoons.

Grouping heavy trucks in the same lane with short following distances can increase road capacity, save fuel, reduce greenhouse gas emissions, and improve safety. These benefits can only be achieved if the vehicle platoon functions in an automated, coordinated manner.

What was unknown about these automated platoons, however, was precisely how far the trucks needed to be from one another to reap the benefits, while still maintaining safety.

A team of advanced vehicle technology experts at Volpe studied truck following behavior on behalf of the Federal Highway Administration (FHWA) to provide baseline statistics for researchers to determine the optimum following distances in automated truck platoons.

The team used naturalistic driving datasets—taken from prior field operational tests of crash avoidance systems—that contain objective data showing how truck drivers drive on freeways in the real world. The results quantified how trucks follow other vehicles in various environmental conditions and at different travel speeds, and how closely a truck follows a lead vehicle when other vehicles cut in.

No other study to date examines the real-world following behavior of heavy trucks on freeways in support of automated truck platooning design, and takes into consideration weather conditions or cut-in behavior from other vehicles, or evaluates the rear-end crash risk of different following distances.

“Our team has extensive expertise in both working with real-world driving data, and with data mining,” said Emily Nodine, team leader in the Advanced Vehicle Technology Division at Volpe. “We designed and executed a data mining algorithm that generated baseline statistics to help researchers determine a safe following distance for truck platooning.”

The team assessed the crash risk of trucks following at different distances by estimating the probability of a rear-end crash using Volpe’s Safety Impact Methodology (SIM) tool. The SIM tool is a computer-based simulation tool that estimates how crash frequency in various driving conflicts would be affected by different conditions. It simulates initial driving conflict conditions and driver/vehicle response based on studies of how drivers respond to different types of driving conflicts.

Key results showed that trucks followed at an average distance of about 2 seconds on freeways in the real world. Trucks generally followed at shorter distances when they were following a passenger car compared to a heavy truck, on state freeways compared to interstates, in clear weather compared to rain or snow, and during the day compared to at night.

Cut-ins generally did not occur when trucks were following a lead vehicle at less than a 1-second following distance, or 25 meters. Rear-end crash risk increased considerably at following distances of less than 1 second for manual response times, and very little crash risk was observed even at 0.5-second following distances for automated vehicle response times.

FHWA and researchers will use the results of this study in developing automated truck platooning applications.

Based on Volpe’s findings, following distances of around 1 second might satisfy the following-behavior criteria of an automated truck platoon; feel natural and comfortable for the truck driver; prevent other vehicles from cutting in between platooning trucks; and minimize crash risk.