Cooperative Adaptive Cruise Control: Human Factors Analysis

Project Background and Objectives

Background

Delay on the Nation's highway systems is a major cost to motorists and businesses, amounting to over $100 billion in lost time and wasted fuel for urban areas in 2010.⁽¹⁾ Congestion has steadily worsened because the population of drivers, number of vehicles, and travel volume continue to increase at a faster rate than system capacity. Miles of travel increased by 76 percent between 1980 and 1999, but miles of highway increased by only 1.5 percent.⁽²⁾

Severe commute congestion is experienced daily by many drivers in urbanized areas. In 1982, the annual average delay per commuter was 14 h. It had climbed to 34 h in 2010 and is forecasted to increase to 41 h by 2020.⁽¹⁾ Large city areas see delays far beyond the national average, such as 74 h in the Washington, DC, metropolitan area in 2010. But congestion also varies significantly from day to day because demand and capacity are constantly changing at any given location, often due to the influence of incidents and other temporary factors. Roughly 40 percent of the average travel delays now occur outside of normal rush-hour periods, limiting predictability, increasing driver frustration, and significantly impacting business production and deliveries.

The effect of congestion depends to a large extent on what users expect in terms of speed, travel time, and delay when these conditions exist. Slowing the growth of congestion and delay improves urban travelers' mobility and productivity and curbs economic inefficiencies. The use of highly integrated Intelligent Transportation Systems (ITS), such as electronic information and communication technology, may extend the capacity of the existing infrastructure system, improving traffic flow and reducing bottlenecks. One proposed ITS technology, CACC, has the potential to address the problem of recurring congestion and reduced mobility.

CACC Concept

The CACC concept envisions drivers sharing vehicle control with an automated system that includes pervasive vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Using dedicated short-range communications (DSRC), vehicles communicate directly with other nearby equipped vehicles to coordinate and adjust longitudinal control through throttle and brake activations. These automated responses occur much more quickly than humanly possible, allowing equipped vehicles to safely travel closer together and increasing the road capacity.

Additionally, equipped vehicles broadcast performance data to roadway infrastructure via DSRC to enable the infrastructure to monitor traffic flow and incidents. Using these data, the infrastructure could develop predictive traffic models and broadcast targets such as speed, following distance, and acceleration and deceleration rates to CACC-equipped vehicles to optimize traffic flow. While the potential throughput benefits are clear in highway environments, infrastructure-emitted information could also promote significant improvements in arterial settings. For example, using broadcasted SPAT information, CACC could influence approaches to red-light intersections to reduce delays, emissions, and fuel consumption.

Although an engaged CACC system would monitor and control a vehicle's speed, drivers would continue to steer their vehicles and be responsible for identifying situations that might require evasive actions. Hence, similar to the use of a conventional cruise control (CCC) system, drivers are assumed to be in control and responsible even though the automated systems are guiding their vehicles at some level. CACC technology would allow cars to travel closer together more safely but is not intended to be a safety system in the same sense as collision warning and stability control systems.

Objectives

Implementing a concept such as CACC entails a large number of factors, the least of which is simply equipping vehicles with the necessary technology. This analytical report attempts to identify a few of the most probable manners in which the CACC concept could be realized and to investigate the human-factors issues that could be associated with such implementations. Possible methods for investigating and addressing these human-factors issues are presented, including test scenarios and the equipment and resources that would be necessary for the research.

The actual research testing is outside the scope of this analysis. Additionally, issues related to system engineering and policy or legal matters (e.g., privacy concerns or responsibility in the case of an accident when a CACC system is engaged) are beyond the reach of this report. It is assumed that the CACC concept, as described in the previous section, is feasible and will function as described.