Samuel J. Levulis

Effects of oncoming vehicle size on overtaking judgments

During overtaking maneuvers on two-way highways drivers must temporarily cross into the opposite lane of traffic, and may face oncoming vehicles. To judge when it is safe to overtake, drivers must estimate the time-to-contact (TTC) of the oncoming vehicle. Information about an oncoming vehicles TTC is available in the optical expansion pattern, but it is below threshold during high-speed overtaking maneuvers, which require a large passing distance. Consequently, we hypothesized that drivers would rely on perceived distance and velocity, and that their overtaking judgments would be influenced by oncoming vehicle size. A driving simulator was used to examine whether overtaking judgments are influenced by the size of an oncoming vehicle, and by whether a driver actively conducts the overtaking maneuver or passively judges whether it is safe to overtake. Oncoming motorcycles resulted in more accepted gaps and false alarms than larger cars or trucks. Results were due to vehicle size independently of vehicle type, and reflected shifts in response bias rather than sensitivity. Drivers may misjudge the distances of motorcycles due to their relatively small sizes, contributing to accidents due to right-of-way violations. Results have implications for traffic safety and the potential role of driver-assistance technologies.

The psychological impact of solitary: A longitudinal comparison of general population and long‐term administratively segregated male inmates

The use of administrative segregation (AS; often used interchangeably with terms such as solitary confinement, supermax, the ‘hole’) in North American prisons remains a common yet increasingly controversial practice. A 2014 survey out of Yale University estimated that nearly 80,000 to 100,000 inmates across the country are being held in restrictive housing units (Liman Program & Association of State Correctional Administrators, 2015). The authors noted that this number is likely an underestimate, as it did not include local jails, juvenile facilities, or military and immigration detention centres. The general purpose of AS was to largely (though not entirely) isolate inmates who are at-risk of harming themselves or others, or who may be the target of harm (Fine & Wingrove, 2014; King, 1999). Among other things, inmates may be assigned to AS for repeat violent behaviour, being labelled an escape risk, riotous behaviour, threatening institutional safety, or being involved in a security threat group (i.e., gang affiliation; Butler, Griffin, & Johnson, 2013).

A Human Factors Perspective on Ethical Concerns of Vehicle Automation

This paper presents a knowledge synthesis of ethical questions for the application of rational ethics theories to human factors in vehicle automation. First, a brief summary of ethical concerns related to transportation automation and human factors is presented. A series of theoretical questions are then posed for different levels of vehicle automation. Particular concerns relating to the Principle of Utility and the Principle of Respect for Persons are highlighted for low levels of automation, high levels of automation, and full automation through the use of theoretical scenarios. Although some recommendations are drawn from these scenarios, the primary purpose of this paper is to serve as a starting point to encourage discussion and collaboration between human factors professionals, engineers, policymakers, transportation officials, software programmers, manufacturers, and the driving public regarding realistic goals for automated vehicle implementation.

Does Perceived Harm Underlie Effects of Vehicle Size on Overtaking Judgments during Driving

Previous research found that participants accepted more gaps during overtaking (in a driving simulator) when the oncoming vehicle was a motorcycle compared with larger vehicles (Levulis, DeLucia & Jupe, 2015). Results were due to the size of the vehicle independently of the type of the vehicle, and represented shifts in response bias instead of sensitivity. The implication is that drivers may perceive motorcycles as being farther away or travelling more slowly than larger vehicles due to their relatively small sizes, contributing to crashes that result from right-of-way violations (Hurt, Ouellet, & Thom, 1981; Pai, 2011). However, in Levulis et al. (2015) vehicle size was correlated with the perceived threat of collision and associated harm posed by the oncoming vehicle (collision with larger vehicles is more harmful than with smaller vehicles). To eliminate this confound, a driving simulator was used to examine whether overtaking judgments are influenced by the size of an oncoming vehicle even when threat of (simulated) collision is removed. The size-arrival effect occurred nevertheless, suggesting that participants relied on perceived distance and speed rather than perceived harm. Countermeasures to misjudgments of gaps during overtaking should include driver-assistance technologies and driver education.

Effects of Touch, Voice, and Multimodal Input, and Task Load on Multiple-UAV Monitoring Performance During Simulated Manned-Unmanned Teaming in a Military Helicopter

Objective: We evaluated three interface input methods for a simulated manned-unmanned teaming (MUM-T) supervisory control system designed for Air Mission Commanders (AMCs) in Black Hawk helicopters. Background: A key component of the U.S. Army’s vision for unmanned aerial vehicles (UAVs) is to integrate UAVs into manned missions, called MUM-T (Department of Defense, 2010). One application of MUM-T is to provide the AMC of a team of Black Hawk helicopters control of multiple UAVs, offering advanced reconnaissance and real-time intelligence of flight routes and landing zones. Method: Participants supervised a (simulated) team of two helicopters and three UAVs while traveling toward a landing zone to deploy ground troops. Participants classified aerial photographs collected by UAVs, monitored instrument warnings, and responded to radio communications. We manipulated interface input modality (touch, voice, multimodal) and task load (number of photographs). Results: Compared with voice, touch and multimodal control resulted in better performance on all tasks and resulted in lower subjective workload and greater subjective situation awareness, ps < .05. Participants with higher spatial ability classified more aerial photographs (r = .75) and exhibited shorter response times to instrument warnings (r = −.58) than participants with lower spatial ability. Conclusion: Touchscreen and multimodal control were superior to voice control in a supervisory control task that involved monitoring visual displays and communicating on radio channels. Application: Although voice control is often considered a more natural and less physically demanding input method, caution is needed when designing visual displays for users sharing common communication channels.

The relationship between global and information processing factors and self-perceived risky driving among older adults

Drivers typically calibrate their driving behavior with their perceived risk of the current driving situation. However, the degree of risky behavior that drivers find acceptable may be affected by individual difference factors, such as gender, cognitive ability, and personality traits. Using a publicly available dataset examining cognitive and personality variables in a sample of older American adults (CogUSA; McArdle, Rodgers, & Willis, 2015), the present study assessed the relationships between global and information processing factors and self-perceived risky driving behavior (after controlling for general self-perceived risk-taking). Global factors included gender, age, and the big five personality traits. Information processing factors were measured by scores on Visual Matching, Incomplete Words, Auditory Working Memory, and Spatial Relations tests. Results indicated that gender, conscientiousness, agreeableness, and visuo-spatial processing predicted increased self-perceived risky driving behavior. The results have implications for the assessment of driving risk factors across ages, as well as the burgeoning field of hazard perception training.

Effects of Touch, Voice, and Multimodal Input on Multiple-UAV Monitoring During Simulated Manned-Unmanned Teaming in a Military Helicopter

A key component of the U.S. Army’s vision for future unmanned aerial vehicle (UAV) operations is to integrate UAVs into manned missions, an effort called manned-unmanned teaming (MUM-T; Department of Defense, 2010). One candidate application of MUM-T is to provide the Air Mission Commander (AMC) of a team of Black Hawk helicopters control of multiple UAVs, offering advanced reconnaissance and real-time intelligence of the upcoming flight route and landing zones. One important design decision in the development of a system to support multi-UAV control by an AMC is the selection of the interface used to control the system, for example, through a touchscreen or voice commands. A variety of input methods is feasible from an engineering standpoint, but little is known about the effect of the input interface on AMC performance. The current study evaluated three interface input methods for a MUM-T supervisory control system used by an AMC located in a Black Hawk helicopter. The evaluation was conducted with simulation software developed by General Electric. Eighteen participants supervised a team of two helicopters and three UAVs as they traveled towards a landing zone to deploy ground troops. A primary monitor, located in front of the participant, presented displays used to monitor flight instruments and to supervise the manned and unmanned vehicles that were under the AMC’s control. A secondary monitor, located adjacent to the participant, presented displays used to inspect and classify aerial photographs taken by the UAVs. Participants were responsible for monitoring and responding to instrument warnings, classifying the aerial photographs as either neutral or hostile, and responding to radio communications. We manipulated interface input modality (touch, voice, multimodal) and workload (rate of photographs to classify). Participants completed three blocks of 8.5-minute experimental trials, one for each input modality. Results indicated that touch and multimodal input methods were superior to voice input. Participants were more efficient with touch and multimodal control (compared to voice), evidenced by relatively shorter photograph classification times, a greater percentage of classified photographs, and shorter instrument warning response times. Touch and multimodal input also resulted in a greater percentage of correct responses to communication task queries, lower subjective workload, greater subjective situation awareness, and higher usability ratings. Multimodal input did not result in significant performance advantages compared to touch alone. Designers should carefully consider the performance tradeoffs when selecting from candidate input methods during system development.

Effects of adjacent vehicles on judgments of a lead car during car following

Objective: Two experiments were conducted to determine whether detection of the onset of a lead car’s deceleration and judgments of its time to contact (TTC) were affected by the presence of vehicles in lanes adjacent to the lead car. Background: In a previous study, TTC judgments of an approaching object by a stationary observer were influenced by an adjacent task-irrelevant approaching object. The implication is that vehicles in lanes adjacent to a lead car could influence a driver’s ability to detect the lead car’s deceleration and to make judgments of its TTC. Method: Displays simulated car-following scenes in which two vehicles in adjacent lanes were either present or absent. Participants were instructed to respond as soon as the lead car decelerated (Experiment 1) or when they thought their car would hit the decelerating lead car (Experiment 2). Results: The presence of adjacent vehicles did not affect response time to detect deceleration of a lead car but did affect the signal detection theory measure of sensitivity d′ and the number of missed deceleration events. Judgments of the lead car’s TTC were shorter when adjacent vehicles were present and decelerated early than when adjacent vehicles were absent. Conclusion: The presence of vehicles in nearby lanes can affect a driver’s ability to detect a lead car’s deceleration and to make subsequent judgments of its TTC. Application: Results suggest that nearby traffic can affect a driver’s ability to accurately judge a lead car’s motion in situations that pose risk for rear-end collisions.

Effects of Oncoming Vehicle Size on Overtaking Judgments

In 2011, 89,000 accidents in the United States involved a vehicle passing another vehicle, which resulted in 740 deaths and 19,000 injuries (NHTSA, 2011). When passing a vehicle on a two-way highway (overtaking), a driver often must temporarily cross into the opposite lane of traffic, and may face oncoming vehicles. To avoid a collision with an oncoming vehicle, the overtaking driver must estimate the time remaining until a collision would occur with the vehicle. Although information about an oncoming vehicle’s time-to-collision is theoretically available in the optical invariant tau (Lee, 1976), it is below threshold during high-speed overtaking maneuvers, which require a large passing distance. Under such conditions, we expect drivers to rely on the oncoming vehicle’s apparent distance and velocity, and thus depth cues such as relative size. We used a driving simulator to determine whether overtaking judgments are influenced by an oncoming vehicle’s size, and on whether such judgments differ between active driving and passive viewing. Twenty-four participants viewed computer-generated scenes in which they were following a lead vehicle on a straight, two-lane, two-way highway. At the start of each scene an oncoming vehicle (motorcycle, car, delivery truck) was visible in the opposite lane. Seven seconds after the scene an auditory tone signaled participants to make an overtaking decision. Participants in the active condition passed the lead vehicle if they thought it was safe to do so. Participants in the passive condition indicated whether it was safe to pass by pressing buttons on the steering wheel. We manipulated the participant’s (and lead vehicle’s) speed (48.28 km/h, 64.37 km/h, 80.47 km/h) and the oncoming vehicle’s speed (72.42 km/h, 88.51 km/h, 104.61 km/h) and distance when the tone occurred (457.20 m, 609.60 m). This resulted in 9 safe and 9 unsafe temporal gaps based on an equation generated from analyses of actual overtaking performance (Gordon & Mast, 1970). Results indicated more accepted gaps and more false alarms (accepted gap when unsafe) in front of motorcycles than larger cars or trucks. The judgments made by participants in the active and passive conditions did not differ. Analyses of signal detection theory measures of sensitivity (d-prime) and response bias (beta) suggested that the effect of vehicle size was due to shifts in response bias rather than sensitivity. Results have implications for traffic safety and for the potential role of driver-assistance technologies.

Effects of Task-Irrelevant Cars on Judgments of Deceleration and Time-to-Contact During Car-Following

Rear-end collisions represent over 25% of crashes with other moving vehicles (NHTSA, 2005). Factors that potentially contribute to such accidents include a driver’s ability to respond to a lead car’s deceleration and to estimate how much time remains until a collision would occur (DeLucia & Tharanathan, 2009). Prior research (Oberfeld & Hecht, 2008) showed that time-to-contact (TTC) judgments of approaching objects were influenced by task-irrelevant distractor objects. This finding has implications for rear-end collisions when drivers must detect a lead car’s deceleration amidst surrounding cars. However, Oberfeld and Hecht simulated a stationary observer rather than a moving observer which is more representative of driving. We measured effects of task-irrelevant distractor cars on judgments of a lead car’s deceleration during simulations of self motion. Observers viewed car-following scenarios in which only a lead car was present or a lead car and two distractor cars were present in adjacent lanes. The lead car decelerated 10 s or 15 s after the scene’s onset, at either 4 ft/s2 (slow) or 10 ft/s2 (fast). The distractor cars decelerated earlier, later, or at the same time as the lead car, or never decelerated. Brake lights were disabled. Observers were instructed to maintain 40 mph and to press a button as soon as the lead car decelerated. Mean response time was significantly longer in the presence of distractor cars when the lead car decelerated 15 s after the scene’s onset, at 4 ft/s2. In a separate study of a TTC (prediction-motion) task used by Oberfeld and Hecht, car-following speed was pre-set to 40 mph, and the lead car decelerated for 3 s and then disappeared. Observers pressed a button when they thought a collision would occur. Distractor cars affected TTC judgments only when deceleration was concurrent with the lead car. In contrast to deceleration detection, TTC judgments were significantly shorter when distractor cars were present compared with absent, suggesting that effects of distractor cars are task-dependent. In summary, task-irrelevant distractor cars can affect deceleration detection and TTC judgments. The implication is that drivers cannot always ignore task-irrelevant cars on the road. Cars on lanes that do not have immediate consequences for a rear-end collision nevertheless may affect a driver’s response to a lead car’s deceleration. Warning systems designed to prevent rear-end collisions may be needed more in relatively dense traffic when drivers are potentially distracted by task-irrelevant cars.