Cooperative Adaptive Cruise Control Human Factors Study

Chapter 1. Introduction

This summary report provides an overview of four human factors experiments conducted in support of the Federal Highway Administration’s (FHWA) connected vehicle program. The methods, findings, and conclusions from these experiments are more completely described in the following FHWA reports:

• FHWA-HRT-16-056, Cooperative Adaptive Cruise Control Human Factors Study: Experiment 1—Workload, Distraction, Arousal, and Trust.⁽¹⁾
• FHWA-HRT-16-057, Cooperative Adaptive Cruise Control Human Factors Study: Experiment 2—Merging Behavior.⁽²⁾
• FHWA-HRT-16-058, Cooperative Adaptive Cruise Control Human Factors Study: Experiment 3—the Role of Automated Braking and Auditory Alert in Collision Avoidance Response.⁽³⁾
• FHWA-HRT-17-024, Cooperative Adaptive Cruise Control Human Factors Study: Experiment 4—Preferred Following Distance and Performance in an Emergency Event.⁽⁴⁾

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 in speed and location of other CACC vehicles, including vehicles that the driver cannot see.⁽⁵⁾

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 would 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. According to Shaldover et al., smaller gaps should subsequently increase the roadway capacity without increasing the amount of roadway.⁽⁶⁾

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 this CACC human factors study was to investigate the effects of CACC on driver performance, workload, situational awareness, and distraction. The goal was not to address all human factors issues associated with CACC use but rather to suggest additional lines of research that might be required to model the influence of human drivers on overall CACC performance.

Cruise control systems, both conventional and adaptive, have been marketed as convenience systems to reduce driver workload and stress by relieving the driver of the need to continuously regulate vehicle speed and following distance.^(7,8) 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).⁽⁹⁾ Combining these newer capabilities with DSRC to comprise a CACC system brings about several possible driver adaptations.

By reducing the driver’s workload or stress, the driver’s physiological arousal level may be reduced. The desired effect of stress reduction would be to optimize the driver’s performance and feelings of well-being. However, the Yerkes-Dodson law holds that for tasks of moderate difficulty, low and high levels of arousal lead to lower levels of performance than some moderate levels of arousal.⁽¹⁰⁾ In accordance with this law, a less favorable CACC outcome might be to reduce driver arousal below the optimum level and result in poorer driver performance. Driver performance continues to be important in semi-autonomous systems such as CACC. In particular, 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. For this reason, several of the experiments conducted for this study assessed drivers’ subjective workload and physiological measures of arousal.

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 the driver’s state of knowledge of his or her vehicle and the environment in which it is operating. Endsley divided 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 mean concerning future system states.⁽¹¹⁾ Intuitively, the situational awareness construct would make sense for examining how the driver and CACC system would 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 was in a platoon of CACC vehicles and relying on the system to maintain a safe gap to the vehicle ahead. In that situation, would the driver be aware of whether the CACC system was functioning correctly and whether the system was capable of correcting a rapidly closing gap? If the driver failed to notice a closing gap that the system was not capable of correcting, then it could be said that the driver lacked situational awareness.

Although the situational awareness construct makes intuitive sense, it was 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 phenomena. As described by Endsley, situational awareness relies on long-term memory; automaticity; various information processing mechanisms (e.g., working memory); and the driver’s goals and objectives, expectations, experiences, and abilities.⁽¹¹⁾ Furthermore, the system, interface design, driver stress, driver workload, and system automation would affect situation awareness, driver decisionmaking, and driver performance.⁽¹¹⁾ This definition of situation awareness would make its measurement problematic unless all of the underlying components could be controlled, manipulated, or measured. Thus, although some of the results of the four experiments reported here may be thought of in terms of driver situational awareness, no attempt was made to directly assess this hypothetical construct.

Four experiments were conducted as part of the CACC human factors study. All four experiments were conducted in a driving simulator. The first three experiments were conducted in the FHWA Highway Driving Simulator. That simulator is described in chapter 2. The fourth experiment was performed in a less sophisticated fixed-base driving simulator, a National Advanced Driving Simulator MiniSim™.

Chapter 2 describes the first experiment, which compared driving with CACC in a string of four or five vehicles with manual control of the following distance in the same size vehicle strings. Chapter 2 also briefly describes calibration of the simulator visuals to ensure that the simulated following distance perceptually matched real-world perception of following distance.

Chapter 3 describes an experiment that explored driver performance when merging into a string of CACC vehicles.

Chapter 4 describes an experiment that took a closer look at the source of a substantial crash reduction benefit obtained with CACC in the first experiment.

Chapter 5 describes an experiment that examined the effect of a driver’s preferred following distance on performance and workload when using short and long CACC gap settings.

Chapter 6 summarizes key findings and recommendations that resulted from the four human factors experiments.