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Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations

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Publication Number:  FHWA-HRT-13-045    Date:  October 2013
Publication Number: FHWA-HRT-13-045
Date: October 2013


Cooperative Adaptive Cruise Control: Human Factors Analysis

Executive Summary

Cooperative Adaptive Cruise Control (CACC) technology presents the possibility of increasing traffic throughput without requiring construction of additional lanes. Direct radio communication between equipped vehicles and roadway infrastructure permits vehicles to travel closer together and better informs drivers of the surrounding driving environment. In addition to improved capacity, CACC presents environmental benefits by increasing efficiency and reducing fuel usage.

As CACC-equipped vehicles can only employ the system when following other equipped vehicles, the quickest realization of highway throughput benefits can be achieved by congregating equipped vehicles in restricted lanes. Once penetration rates rise, benefits will expand to all highway lanes and significant capacity increases will be possible. Benefits can also be gained in arterial intersection environments. The CACC system capabilities supplemented by signal phase and timing (SPAT) information from the infrastructure can inform drivers of the most efficient speed at which to approach and pass through an intersection. This information would not only save time for drivers but also reduce emissions and fuel usage.

Although already demonstrated as technically feasible, CACC faces many hurdles related to the abilities and limitations of the humans using the system. Numerous human-factors-related issues may impact the success of a new system and need to be addressed before implementation can be considered. Application, use, reliance, and trust of automation have numerous pitfalls, all exacerbated when applied to dynamic and fast-pace environments such as highway travel. Additionally, the effect CACC usage may have on a driver's workload (increase or decrease) directly impacts performance, safety, and overall situation awareness (SA). CACC system success relies on an accurate understanding of general driving behaviors such as car-following, lane position variability, and lane-changing. Each of these is heavily influenced by a variety of both deliberate and reflexive human judgments, which are prone to errors and misguided decisions.

To determine how these human-factors issues affect CACC technology, research scenarios are presented for exploration. The proposed studies involve several research methodologies, including microsimulation, low- and high-fidelity simulation, and field research. Data gathered from this future research will be crucial to the success of CACC technology and can probably be applied to other driving-related automation.