Expanding the Freight Capacity of America's Highways : Platooning and Connectivity to Increase Efficiency : [fact sheet]

NHTSA · 2017 · ROSA P / United States. Federal Highway Administration

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Summary

This fact sheet from the Federal Highway Administration’s (FHWA) Exploratory Advanced Research (EAR) Program addresses the projected 40 percent increase in freight tonnage and doubling of freight value on U.S. highways over the next 25 years. To mitigate the resulting capacity constraints, the program investigates truck platooning and connectivity as methods to increase highway efficiency. The research is conducted through two primary projects: one at Auburn University, in partnership with industry leaders like Peterbilt and Peloton Technology, and another at the University of California, Berkeley, in partnership with Volvo Technologies. These initiatives aim to develop technologies that allow Class 8 trucks to travel in close formation, thereby maximizing aerodynamic and operational efficiencies. The core technology under investigation is Cooperative Adaptive Cruise Control (CACC), which integrates single-vehicle adaptive cruise control with onboard sensors (radar, GPS, video cameras) and vehicle-to-vehicle communication via Dedicated Short-Range Communication (DSRC). This system exchanges operational data at 10 Hz, allowing trucks to automatically adjust engine and brake inputs to maintain precise longitudinal control, including speed and separation distance. While drivers retain responsibility for lateral control and traffic monitoring, the CACC system enables trucks to travel closer together than manual operation permits. The Auburn team focuses on business case analysis, aerodynamics, and traffic modeling for two-truck platoons, while the UC Berkeley team tests three-truck platoons in real highway traffic using a "constant time gap" strategy, where separation distance is proportional to speed. Field trials conducted by the Auburn team demonstrated significant fuel savings in two-truck platoons. At a 30-foot separation, the combined fuel savings peaked at nearly 7 percent, with the leading truck saving roughly 5 percent and the trailing truck saving up to 10 percent. At a more realistic 50-foot following distance, the trailing truck achieved savings exceeding 10 percent. The UC Berkeley trials assessed system responsiveness to real-world disruptions, such as lane changes by other vehicles, confirming that the control system can automatically designate a new lead vehicle and re-establish following gaps. The significance of these findings lies in the potential to reduce the high operational costs of freight transport, where fuel is the largest single cost component per mile. Auburn researchers identified that private fleets and large over-the-road haulers operating trips longer than 500 miles are best positioned to realize returns on the initial investment in CACC technology. Furthermore, the research suggests that once market penetration reaches 60 percent on specific routes, platooning could improve overall highway traffic flows. Ultimately, the EAR Program concludes that platooning and CACC can reduce driver workload, lower emissions, and decrease fuel expenses, offering a viable solution for expanding freight capacity without extensive infrastructure expansion.

Key finding

On-road two-truck platoons achieved a peak combined fuel saving of nearly 7 percent at a 30 ft gap (about 5 percent for the lead truck and up to 10 percent for the trailing truck).

Methodology

on_road

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (8 acquisition events logged).

StageOutcomeToolModelPromptAttemptsCompleted
discover success rosap 2 2026-05-23
archive success 1 2026-05-23
extract success cached 2 2026-06-10
clean success 1 2026-06-01
chunk success 1 2026-06-01
embed success 1 2026-06-02
enrich success 1 2026-05-23
promote success 1 2026-05-23
summarize success llm qwen3.6-27b-prismaquant summ-v5 3 2026-06-10
tag success vector_similarity 24 2026-06-11
verify success 4 2026-06-10

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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