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Publication Number:  FHWA-HRT-16-056     Date:  December 2016
Publication Number: FHWA-HRT-16-056
Date: December 2016


Cooperative Adaptive Cruise Control Human Factors Study: Experiment 1—Workload, Distraction, Arousal, and Trust



Cooperative adaptive cruise control (CACC) combines the following three driver assist systems: (1) conventional cruise control, which automatically maintains the speed a driver has set, (2) adaptive cruise control (ACC), which uses radar or light detection and ranging sensors to automatically maintain a gap the driver has selected between the driver’s vehicle and a slower moving vehicle ahead, and (3) dedicated short-range communications (DSRC) to transmit and receive data with surrounding vehicles so that the cruise control system can more quickly respond to changes and speed and location of other CACC vehicles, including vehicles that the driver cannot see.(1)

When using CACC, drivers share vehicle control with an automated system that includes vehicle-to-vehicle and vehicle-to-infrastructure communications. Communications between nearby CACC-equipped vehicles enable automated coordination and adjustment of longitudinal control through throttle and brake activations. Automated control should enable CACC-equipped vehicles to safely travel with smaller gaps between vehicles than drivers could safely manage on their own. Smaller gaps should subsequently increase the roadway capacity without increasing the amount of roadway.

Although technically feasible from computational and communications perspectives, the ability of users to safely interact with CACC-equipped vehicles in the scenarios envisioned by engineers has yet to be demonstrated. The goal of the human factors research of which this study is a part is to investigate the effects of CACC on driver workload, situational awareness, and distraction. The goal is not to address all human factors issues associated with CACC platooning but rather to serve as an initial experiment that may suggest additional lines research that may be required to model the influence of human drivers on overall CACC performance.

Current cruise control systems, both conventional and ACC, are marketed as convenience systems that reduce driver workload and stress by relieving the driver of the need to continuously regulate vehicle speed and following distance.(2,3) Some newer adaptive systems have been combined with forward collision warning and forward collision avoidance systems. Collision avoidance systems may have full braking authority (i.e., they may have the maximum deceleration possible and may brake to a full stop when necessary).(4) When these newer capabilities are combined with DSRC to comprise a CACC system, several driver adaptations
are possible.

By reducing the drivers’ workload or stress, their physiological arousal levels may be reduced. The desired effect of stress reduction would be to optimize the drivers’ performances and feelings of well being. However, the Yerkes-Dodson law suggests that for tasks of moderate difficulty, low and high levels of arousal will lead to lower levels of performance than some moderate levels of arousal.(5) In accordance with this law, a less favorable CACC outcome might be to reduce driver arousal below the optimum level and may result in poorer driver performance. Driver performance remains important in semi-autonomous systems such as CACC. CACC systems do not maintain lateral control of the vehicle, and braking is not always the best or safest response to a slower or stopped vehicle ahead. One method of evaluating the effects of CACC on drivers would be to monitor changes in physiologic arousal level and to observe the relationship between those changes and driver performance. Of concern would be a finding that CACC reduces driver workload thereby resulting in low levels of physiological arousal that in turn result in reductions is measures of driver performance. Performance in this experiment was assessed in terms of drivers’ ability to avoid a collision.

It is not a given that the workload relief provided by a CACC system would result in a lower level of physiological arousal or that workload relief would have the same effect on all drivers. The task difficulty homeostasis model would predict that if the CACC system caused an undesirable lowering arousal, drivers might engage in behaviors that would increase arousal back to a desirable level.(6) In his task difficulty homeostasis model, Fuller suggests that drivers will manage speed to regulate task difficulty.(6) However, the system controls speed in a CACC platoon, which forces drivers to find another way to regulate task difficulty. Several researchers have observed that the alternative of choice is to engage in secondary (non-driving) tasks.(7–9)

Jamson et al. conducted a driving simulator study in which participants drove in manual and fully automated mode on a simulated motorway (the equivalent of a six-lane freeway in the United States).(7) Conditions with light and heavy traffic were simulated (600 and 1,800 vehicles/lane/h, respectively). The fully automated mode consisted of ACC plus lane control, and participants were free to engage and disengage it. The findings showed that, on average, drivers were willing to drive at lower speeds with fewer lane changes in the fully automated mode.(7) With light traffic, drivers in fully automated mode engaged in more secondary tasks and had less gaze time to the forward roadway than drivers in manual mode or drivers in fully automated mode and heavy traffic. Consistent with Fuller’s task difficulty hypothesis, drivers in fully automated mode and light traffic had lower heart rates and more eye-closure time than drivers in the other conditions.(6)

Llaneras et al. reported on a test-track experiment with a vehicle equipped with both ACC and lane keeping.(8) When both ACC and lane keeping were active, drivers engaged in many more secondary tasks than when driving with ACC alone. Glances away from the forward roadway with the combined lane keeping and ACC systems were also reported to be more frequent and slightly longer than with ACC alone.

If increasing levels of automotive automation lead to a greater willingness to engage in secondary tasks and glances away from the roadway, it can be hypothesized that drivers’ awareness of their environment (i.e., situational awareness) would decline.

Situational awareness is defined as the state of knowledge of an operator of a complex dynamic system. In the context of CACC, situational awareness refers to a driver’s state of knowledge of his or her vehicle and the environment in which it is operating. Endsley breaks this state of knowledge into three parts: (1) knowledge of all the elements of the system that are related to the driver’s goals, (2) what the state of those elements means relative to those goals, and (3) what the current state means concerning future system states.(10,11) Intuitively, the situational awareness construct makes sense for examining the how the driver and CACC system will perform together. With the CACC system, the driver must be aware of the mode of the system (i.e., manual, conventional cruise control, ACC, or CACC), lane position, the presence and trajectory of other vehicles, obstacles, and orientation relative to intended destination. It is possible to imagine a situation in which the driver is in a platoon of CACC vehicles and relying on the system to maintain a safe gap to the vehicle ahead. In that situation, will the driver be aware of whether the CACC system is functioning correctly and whether the system is capable of correcting a rapidly closing gap? If the driver fails to notice a closing gap that the system is not capable of correcting, then it could be said that the driver lacked situational awareness.

Although the situational awareness construct makes intuitive sense, it is built on a number of other theoretical constructs, each of which is based on paradigms and theories that are intended to organize and explain various behavioral phenomenon. As described by Endsley, situation awareness relies on long-term memory, automaticity, and various information processing mechanisms (e.g., working memory), as well as the driver’s goals and objectives, expectations, experiences, and abilities.(10) Furthermore, the system, interface design, driver stress, driver workload, and system automation will affect situation awareness, driver decision-making, and driver performance.(10) This definition of situation awareness makes its measurement problematic unless all of the underlying components can be controlled, manipulated, or measured.

Chapter 2 describes an experiment conducted in a driving simulator in which the effects of simulated CACC on workload, physiological arousal, distraction, crash avoidance, and trust in the CACC system were assessed. Situational awareness was not assessed in this experiment. Use of a driving simulator imposes limitations on the conclusions that can be reached regarding drivers’ responses to the use or non-use of CACC. The two major concerns are (1) differences between a simulated driving environment and a real one and (2) that in a single simulated driving session, regardless of length, long-term driver adaptations (i.e., adaptations that might take place over days, weeks, or years) cannot be assessed. Precautions to partially mitigate the former concern are described in chapter 2. It is unlikely that there is any practical way to assess long-term adaptions in a driving simulator, and that concern will need to be addressed with observation of long-term use in the real world.



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