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When analyzing the causes of an accident, it is critical to determine whether the driver could have prevented the accident. In previous studies on the reaction times of drivers, the definition and values of reaction times vary, so applying reaction time is difficult. In such analysis, the driver’s reaction time from perception is required to determine whether the driver could have prevented the accident, but past studies are difficult to utilize in accident analysis as reaction time measurements were taken after the occurrence of hazardous situations. In this study, 93 subjects from age groups ranging from 20 s to 40 s participated in an experiment inside a full-scale driving simulator, to determine reaction time values that can be practically applied to accident analysis. A total of 4 hazardous accident situations were reproduced, including driving over the centerline, pedestrian jaywalking, a vehicle cutting in, and intersection traffic signal violation. The Time-To-Collision (TTC) was 2.5 s and the driving speed was set to the common city road speed limits of 60 and 80 km/h. An eye tracker was used to determine the driver’s Saccade Latency (SL) during hazardous situations. Brake Reaction Time from Perception (BRTP), Steer Reaction Time from Perception (SRTP), and Driver Reaction Time from Perception (DRTP) were derived, and the measurements were statistically analyzed to investigate differences by age group, gender, speed, and type of hazardous situation. Most participants were found to avoid collisions by braking first rather than steering for the presented hazardous situations, except for the cutting in situation. Also, to determine a reaction time that would cover most drivers, the 85th percentile of DRTP was calculated. The 85th percentile of DRTP was in the range of 0.550 – 0.800 s. Specifically for each hazardous situation, it was 0.650 s for driving over the centerline, 0.800 s for the pedestrian jaywalking, 0.660 s for cutting in, and 0.550 s for the intersection traffic signal violation. For all 4 hazardous situations combined, the 85th percentile of DRTP was 0.646 s. The findings can be utilized to determine the driver’s likelihood of avoiding accidents when faced with similar hazardous situations.

INTRODUCTION: In the present study, an Adaptive Ground Control System for Multiple-UAV Operator Workload Decrement (AGCS) has been developed and the effectiveness of the system has been analyzed using eye-tracking and task performance data. The AGCS contained four more functions than the conventional GCS (CGCS) functions. The functions were based on real-time operator gaze information, multiple UAV operational state, and mission state information to help safe and efficient multiple UAV operation.METHODS: A total of 30 volunteers participated in the human-in-the-loop experiment to compare the performances of the newly developed AGCS and CGCS while executing reconnaissance and strike missions by operating multiple UAVs.RESULTS: According to the results, the AGCS demonstrates a statistically significant increase in mission performance, such as the mission completion rate (M = 97.3 vs. M = 95.4; SD = 3.1 vs. SD = 4.9) and mission success rate (M = 90.4 vs. M = 88.4; SD = 5.7 vs. SD = 5.6). In addition, the subjects' pupil diameter and gaze indicator show significant differences in the direction of workload reduction (α = 0.05). The subjects expressed positive opinions about using the AGCS.DISCUSSION: The originally developed AGCS showed a promising future extension based on the experimental data. After completion of the experiment, domain experts were interviewed and the next version will reflect their opinion.Lim H-J, Choi S-H, Oh J, Kim BS, Kim S, Yang JH. Adaptive ground control system of muliple-UAV operators in a simulated environment. Aerosp Med Hum Perform. 2019; 90(10):841-850.

In the present study, the effects of driver distraction were investigated using a systematic evaluation method based on simulator experimental data. Mobile devices and in-vehicle systems intended for driver safety and convenience could actually serve as major factors causing driver error by increasing activities that interrupt driving. A total of 11 volunteers participated under different distracted highway driving environment conditions, such as normal driving, visual-manual load, and cognitive overload. Drivers' steering control response, lateral vehicle motion, and eye-tracking response were measured. The following results indicated significant (α=0.05) differences compared to those of the normal driving condition: Under visual-manual load condition, the steering reversal rate, steering angular velocity, lateral vehicle motion, gaze range, and saccade increased, but the percentage of gaze on the road ahead decreased. Under cognitive load condition, the steering reversal rate, lateral vehicle motion, and percentage of gaze on road ahead increased, but the gaze range and saccade decreased. Different effects on driving were observed according to the driving workload modalities. Further research needs to be performed to generalize the results with an increased sample size, diverse driving environments, and accurate sensors.

In the current study, the effects of driver distraction were investigated using a systematic evaluation method based on real vehicle experimental data. A total of 15 volunteers participated under different distracted highway driving environment conditions, such as 1) normal driving, 2) visual load, 3) visual-cognitive load, 4) visual-manual load, 5) cognitive underload, and 6) cognitive overload. Drivers' steering control response and lateral vehicle motion were measured. The following results indicated significant (α=0.05) differences compared with those of the normal driving condition: Under visual load condition, the steering angular velocity and lateral vehicle motion increased. Under visual-cognitive condition, the steering reversal rate, steering angular velocity, and lateral vehicle motion increased. Under visual-manual load condition, the steering reversal rate, steering angular velocity, and lateral vehicle motion increased. Under cognitive underload condition, the steering reversal rate increased. Under cognitive load condition, the steering reversal rate, and lateral vehicle motion increased. Different effects on driving on real roads were observed according to the driving workload modalities. Further research needs to be performed to generalize the results with an increased sample size and diverse driving environments.

* Graduate School of Automotive Engineering, Kookmin Univ. (Received July 2, 2014; Revised January 12, 2015; Accepted March 10, 2015)Key Words: Adaptive Cruise Control(적응순항제어), Formal Analysis(정형 분석), Intelligent Vehicle(지능형 차량), Mode Confusion(모드 혼동), User Interface(사용자 인터페이스)초록: 최근 과학 기술의 발전으로 첨단 자율 기능을 탑재된 차량의 출현이 가능하게 되었으며, 기존 차량에 추가된 첨단 운전자 지원 시스템(Advanced Driver Assistance Systems: ADAS)은 기존 차량에 추가된 이러한 자율 시스템들의 좋은 예라 할 수 있다. 이러한 시스템은 다수의 작동 모드를 가지는데 운전자들이 현재 수행중인 작동 모드를 잘못 인지하게 되면 교통사고가 일어날 수 있음이 관찰되고 있다. 본 연구에서는 적응 순항제어(Adaptive Cruise Control: ACC) 시스템을 장착한 자동화된 자동차의 운전자 인터페이스를 설계하고 이를 정형 기법을 통한 분석과 피실험자를 이용한 실험을 통해 모드 혼동 발생 여부를 검증하고 이를 개선하는 연구를 수행하였다. 이를 통해 모드 혼동을 방지하기 위해서는 시스템에 대한 심성 모델의 정확성과 간결성뿐만 아니라 인터페이스 구현시 디스플레이의 명확성이 또한 매우 중요하다는 것을 확인하였다.Abstract: Recent development in science and technology has enabled vehicles to be equipped with advanced autonomous functions. ADAS (Advanced Driver Assistance Systems) are examples of such advanced autonomous systems added. Advanced systems have several operational modes and it has been observed that drivers could be unaware of the mode they are in during vehicle operation, which can be a contributing factor of traffic accidents. In this study, possible mode confusions in a simulated environment when vehicles are equipped with an adaptive cruise control system were investigated. The mental model of the system was designed and verified using the formal analysis method. Then, the user interface was designed on the basis of those of the current cruise control systems. A set of human-in-loop experiments was conducted to observe possible mode confusions and redesign the user interface to reduce them. In conclusion, the clarity and transparency of the user interface was proved to be as important as the correctness and compactness of the mental model when reducing mode confusions.

This paper aims to redefine the unfamiliar and ambiguous ideas related to autonomous driving such as "Is it permissible not to hold the steering wheel during autonomous driving"?, "Should the driver keep his/her eyes looking forward"?, "Can the driver sleep"?, "Is distraction always or sometimes possible"?, and "To what extent are non driving-related activities allowed in the autonomous vehicle"? These types of issues are examined from the viewpoint of drivers, not from automotive technical experts. In addition, after re-defining human-centered automation, this paper intends to analyze and compare it with the levels of tech-focused automation and utilize it as an indicator for needs of novel HMI(Human-Machine Interface)s of autonomous vehicles.

Despite the increased use of sensor technologies, including unmanned vehicles, the vast majority of improvised explosive device (IED) detections are made by human vision. Thus, TRAC-Monterey developed a simulation-based training prototype called the perceptual learning trainer (PLT). Fourteen novice and 5 expert IED detectors participated in human-in-the-loop experiments in which all participants were trained using the PLT tool while their eye-movement and IED detection performance were tracked in real-time. A series of 100 IED images with various degrees of difficulty was used for the training session. Preand posttraining assessments were conducted. Both speed and accuracy improved after just 1 session of the PLT training: RT decreased by 3.7 s for novices (p .001) and 3.4 s for experts (p .031), and detection probability increased by 5.9% for novices (p .001). The PLT tool improved IED detection performance more in novice IED detectors than in experts. Novices and experts showed different visual scan patterns.

We present recent human-vehicle interaction (HVI) research conducted in the area of defense and military application. Research topics discussed in this paper include: training simulation for overland navigation tasks; expertise effects in overland navigation performance and scan patterns; pilot’s perception and confidence on an overland navigation task; effects of UAV (Unmanned Aerial Vehicle) supervisory control on F-18 formation flight performance in a simulator environment; autonomy balancing in a manned-unmanned teaming (MUT) swarm attack, enabling visual detection of IED (Improvised Explosive Device) indicators through Perceptual Learning Assessment and Training; usability test on DaViTo (Data Visualization Tool); and modeling peripheral vision for moving target search and detection. Diverse and leading HVI study in the defense domain suggests future research direction in other HVI emerging areas such as automotive industry and aviation domain.

Helicopter overland navigation is a cognitively complex task that requires continuous monitoring of system and environmental parameters and many hours of training to master. This study investigated the effect of expertise on pilots' gaze measurements, navigation accuracy, and subjective assessment of their navigation accuracy in overland navigation on easy and difficult routes.A simulated overland task was completed by 12 military officers who ranged in flight experience as measured by total flight hours (TFH). They first studied a map of a route that included both easy and difficult route sections, and then had to 'fly' this simulated route in a fixed-base helicopter simulator. They also completed pre-task estimations and post-task assessments of the navigational difficulty of the transit to each waypoint in the route. Their scan pattern was tracked via eye tracking systems, which captured both the subject's out-the-window (OTW) and topographical map scan data.TFH was not associated with navigation accuracy or root mean square (RMS) error for any route section. For the easy routes, experts spent less time scanning out the window (p = 0.61) and had shorter OTW dwell (p = -0.66). For the difficult routes, experts appeared to slow down their scan by spending as much time scanning out the window as the novices while also having fewer Map fixations (p = -0.65) and shorter OTW dwell (p = -0.69). However, TFH was not significantly correlated with more accurate estimates of route difficulty.This study found that TFH did not predict navigation accuracy or subjective assessment, but was correlated with some gaze parameters.

The article of record as published may be found at http://dx.doi.org/10.1177/107118312561399 Helicopter overland navigation is a cognitively complex task that requires continuous monitoring of system and environment parameters and many hours of training to master. This study investigated the effect of expertise on pilots’ gaze measurements, navigation accuracy, and subjective assessment of their navigation accuracy in overland navigation on easy and difficult routes. Twelve military officers who ranged in flight experience, as measured by total flight hours (TFH) completed a simulated overland task. They first completed map study of a route comprised of easy and difficult route sections, and then had to ‘fly’ this simulated route in a fixed-base helicopter simulator. They also completed pre-task estimations and posttask assessments of how hard it would be to navigate to each waypoint in the route. Their scan pattern was tracked via two eye tracking systems. The tracking systems captured both the participant’s out-the-window (OTW) and topographical map scan data. TFH was not associated with navigation accuracy and RMS (root mean square) error for either legs. For the easy routes, experts spent less time scanning out the window (ρ=- .61), had shorter OTW dwell (ρ=-.66), For the difficult routes, experts appeared to slow down their scan by spending as much time scanning out the window as novices, while also having fewer MAP fixations (ρ=-.65) and shorter OTW dwell (ρ=-.69). However, TFH was not significantly correlated with more accurate estimates of route difficulty. This study found that TFH did not predict navigation accuracy or subjective assessment but was correlated with some gaze parameters. It may be that TFH is too crude measure to use as a measure of expertise for task specific activities (e.g., overland navigation). Approved for public release; distribution is unlimited.

The article of record as published may be located at http://dx.doi.org10.3357/ASEM.2947.2011 Introduction: Helicopter overland navigation is a cognitively complex task that requires continuous monitoring of system and environment parameters and years of training. This study investigated potential improvements to training simulation by analyzing the influences of flight expertise on visual scan patterns. Methods: There were 12 military officers who varied in flight expertise as defined by total flight hours who participated in overland navigation tasks. Their gaze parameters were tracked via two eye tracking systems while subjects were looking at out the window (OTW) and topographic Map views in a fixed based helicopter simulator. Results: Flight performance measures were not predicted by the expertise level of pilots. However, gaze parameters and scan management skills were predicted by the expertise level. For every additional 1000 flight hours, on average, the model predicted the median dwell will decrease 28 ms and the number of view changes will increase 33 times. However, more experienced pilots scanned more OTW than novice pilots, which was contrary to our expectation. A visualization tool (FEST: Flight and Eye Scan visualization Tool) to replay navigation tasks and corresponding gaze data was developed. Qualitative analysis from FEST revealed visual scan patterns of expert pilots not only looking ahead on the map, but also revisiting areas on the map they just flew over to retain confidence in their orientation. Discussion: Based on the analysis provided above, this work demonstrates that neurophysiological markers, such as eye movements, can be used to indicate the aspects of a trainee’s cognitive state that are useful for cuing an instructional system.

This paper proposes an interactive module of human control model using SIMULINK in MATLAB, taking into account the various aspects of the individual driving characteristics. Although, extensive studies have shown the relationships of age, amount of alcohol intake, and sleep deprivation level with driving performance, little attention has been paid to the development of interactive human driver models that can be linked to vehicle simulators After modeling the longitudinal and lateral control behaviors of normal drivers, these contributory factors are integrated into a baseline driver model in this paper. Correspondingly, an interactive driver module (IDM) was developed including features which enable to tune the individual driving characteristics such as age, amount of alcohol intake, and sleep deprivation level. The IDM prototype was implemented with PreScan to show potential human -- vehicle interaction simulation capabilities.

This study aims to validate vehicle and physiological readouts for the assessment of a driver's state. According to the Korea Transportation Safety Authority's report (2012), 22.5% of 1000 drivers have experienced a crash or near-crash caused by drowsy or distracted driving. In this paper, 2 simulated driving-environment experiments were designed. One experiment was performed to analyze various characteristics of drowsy drivers. Four subjects participated in 2 experimental sessions with 2 different sleep durations the day prior to the experiment (above 7 hours of sleep versus below 4 hours of sleep). Subjects were expected to drive on a highway road at 80 km/h. Another experiment was designed to analyze the characteristics of distracted or high workload drivers. Sixteen subjects participated in 1 experimental session requiring different levels of distraction or difficulty (e.g., driving while conducting a secondary task versus driving only). Subjects were expected to drive on a downtown road at 40 or 60 km/h depending on the level of difficulty. The present study indicated that vehicle and physiological data have the potential to assess drowsiness, distraction, and high workload driving. The vehicle and physiological data validated in this study will be incorporated into a driver's state-assessment algorithm in the future.

This paper presents the application of the agent-based multimethod simulation approach to the development of the Unmanned Ground Vehicle(UGV) simulation testbed prototype to support both concept exploration and the performance-based requirement analysis of future UGVs. We describe the UGV simulation testbed prototype (UGVSimp) incorporating 1) UGV mission scenario, 2) the operational concepts of UGVs with the automation level, such as the autonomous unmanned operation, the autonomous manned operation with human operators, and the autonomous autocontrolled operation with Command and Control(C2), 3) UGV functionality, including move, fire, rescue, 4) operator’s task processes constrained by the automation level, human resources(visual, auditory, cognition, psychomotor), and the associated human machine interface of the control station. Furthermore, we show some experimental results for the UGV’s competing operational concepts for the three cases(autonomous unmanned, autonomous manned, autonomous autocontrolled), using UGVSimp.

This paper proposes a probabilistic method for calculating collision alert thresholds, taking into consideration of human behavioral factors. Many existing alerting algorithms do not consider human factors. However, the performance of active safety systems in smart vehicles can be improved by including consideration of human factors in a systematic manner. In this paper, we present a method for developing alerting thresholds based on anticipating the consequences of alerting or not alerting a driver to a collision risk. An intersection scenario and a front collision warning scenario are used to demonstrate the proposed probabilistic performance-based method.

In this study, we developed a new driver interface for an adaptive cruise control system that suppresses mode confusion, based on a formal method for analyzing the correctness and succinctness of the interface and information. In the finite-state transition model for the interface, the states were grouped into a set of modes, each of which consists of the states that transition to the same state for the same user-triggered event. To test the mode awareness of our new interface and compare it with the mode awareness of a conventional interface, a set of human-in-the-loop experiments were conducted in a simulated environment using a driving simulator.

INTRODUCTION: This paper aims to provide insights into human perception, navigation performance, and confidence in helicopter overland navigation. Helicopter overland navigation is a challenging mission area because it is a complex cognitive task, and failing to recognize when the aircraft is off-course can lead to operational failures and mishaps. METHODS: A human-in-the-loop experiment to investigate pilot perception during simulated overland navigation by analyzing actual navigation trajectory, pilots' perceived location, and corresponding confidence levels was designed. There were 15 military officers with prior overland navigation experience who completed 4 simulated low-level navigation routes, 2 of which entailed auto-navigation. This route was paused roughly every 30 s for the subject to mark their perceived location on the map and their confidence level using a customized program. RESULTS: Analysis shows that there is no correlation between perceived and actual location of the aircraft, nor between confidence level and actual location. There is, however, some evidence that there is a correlation (rho = -0.60 to approximately 0.65) between perceived location and intended route of flight, suggesting that there is a bias toward believing one is on the intended flight route. DISCUSSION: If aviation personnel can proactively identify the circumstances in which usual misperceptions occur in navigation, they may reduce mission failure and accident rate. Fleet squadrons and instructional commands can benefit from this study to improve operations that require low-level flight while also improving crew resource management. Language: en

Continual advances in technology, along with increased cockpit workload-- particularly the shift from two-seat to single-seat fighters to save money and reduce risk to life--push the limits of human mental capacity. Additionally, there is interest within the military aviation community to integrate Unmanned Aerial Vehicle (UAV) control into the cockpit in order to expand force projection capability. This study compared the effects on formation flight performance of two different secondary tasks, specifically a traditional secondary task such as target prosecution with an electro-optical Forward Looking Infra-Red (FLIR) pod, and a futuristic secondary task such as UAV supervisory control. A total of 34 military fighter aviators volunteered to fly three five-minute F-18 simulator sessions in close formation with no secondary task, and then treated with each of the two secondary tasks. Results provided clear indication that the futuristic task was significantly more challenging than the traditional task, and that both secondary tasks significantly increased the average mean following distance and variance compared to the undistracted flying baseline scenario. Additionally, we found no evidence that increased flight experience (total flight hours) significantly improved performance of the prescribed primary task when treated with the futuristic task distraction. Knowledge gained from the results could contribute to improved crew resource management (CRM) and pilot workload management as well as flight safety resulting from the modification of flight procedures based on known effects of distractions in the cockpit.

This study compared the eye scan patterns of nonexperienced and experienced helicopter pilots during a simulated high-speed, low-level flight. For helicopters, flying at high speeds and low levels is not the safest way to fly, but in times of war, it is necessary for survival. Subjects were 17 active-duty Navy helicopter pilots, all of whom had different levels of flight experience based on their total flight times. Each pilot was asked to successfully navigate a course through a simulated southern California desert in a fixed-based helicopter simulator modeled after the U.S. Navy's MH-60S. The pilots' eye scanning behavior was tracked by an eye-tracking system while they flew the course to determine scan rate and locations. Flight parameters, such as air speed and altitude, were recorded by the simulator's recording system. Analysis of the results obtained from the eye tracking system showed a decreasing relationship between scan rate and pilot experience, indicating that the scan rate decreases as a pilot becomes more experienced. The analysis used altitude variance as a measure of flight performance. Results indicate that higher scan rates correlate with higher degrees of variance in the altitude, indicating that a quicker scan does not necessarily result in better performance. The more experienced pilots showed a lower degree of altitude variance overall (they were more consistent in maintaining a constant altitude above the ground), yet all of these pilots exhibited slower scan rates. The integration of eye tracking technology with a simulator representing an aircraft currently in service was a success. Although none of the null hypotheses presented were rejected, trends were evident in scan rates when compared with pilot experience. The relatively small sample size was identified as the major reason for the lack of significance of the results. Data were lost on five subjects from both the eye tracking system and the simulator's flight recording software.

In this paper, we describe a framework for developing an interactive feedback model of manned-unmanned teaming (MUT) operational mode selections for a broad spectrum of unmanned vehicle (UV) autonomy levels. Though the highest autonomy levels are within reach as technology continues to advance, lower level autonomy or human manual control will still be needed depending on mission scenarios and dynamic situations. Understanding when and how we change the autonomy level of MUT is critical to ensure system safety and to maximize system performance. Thus, we propose to integrate feedback from various human state variables (i.e., physiological and behavioral signals such as heart rate, skin conductance level, and postures) for estimating human workload and interest level and key task performance measures (accuracy and speed for assigned missions, task interaction) into MUT systems so that the MUT adapts its mode automatically as needed. We developed RESCHU-SA (Research Environment for Supervisory Control of Heterogeneous Unmanned Vehicles Swarm Attacks), a modified version of the RESCHU simulator originally developed at MIT. We designed a human-in-the-loop experiment to collect baseline data for varying levels of autonomy using the RESCHU-SA along with a physiological sensor BioHarness. Different levels of autonomy include 1) high level autonomy using an auction algorithm or nearest-neighbor assignment algorithm, 2) low level autonomy using manual assignment, and 3) interactive autonomy which allows operators to change between high and low autonomy level. The purpose of the research is to investigate the level of autonomy that should be given to unmanned vehicles (UVs) to successfully complete a mission using a MUT in a swarm attack scenario.

The article of record as published may be located at http://dx.doi.org/10.3357/ASEM.3230.2012 Introduction: Most target search and detection models focus on foveal vision. In reality, peripheral vision plays a signifi cant role, especially in detecting moving objects. Methods: There were 23 subjects who participated in experiments simulating target detection tasks in urban and rural environments while their gaze parameters were tracked. Button responses associated with foveal object and peripheral object (PO) detection and recognition were recorded. In an urban scenario, pedestrians appearing in the periphery holding guns were threats and pedestrians with empty hands were non-threats. In a rural scenario, non- U.S. unmanned aerial vehicles (UAVs) were considered threats and U.S. UAVs non-threats. Results: On average, subjects missed detecting 2.48 POs among 50 POs in the urban scenario and 5.39 POs in the rural scenario. Both saccade reaction time and button reaction time can be predicted by peripheral angle and entrance speed of POs. Fast moving objects were detected faster than slower objects and POs appearing at wider angles took longer to detect than those closer to the gaze center. A second-order mixed-effect model was applied to provide each subject’s prediction model for peripheral target detection performance as a function of eccentricity angle and speed. About half the subjects used active search patterns while the other half used passive search patterns. Discussion: An interactive 3-D visualization tool was developed to provide a representation of macro-scale head and gaze movement in the search and target detection task. An experimentally validated stochastic model of peripheral vision in realistic target detection scenarios was developed.

*† ‡ § In this paper, we present a method for computing the Probability of Conflict, PC, using a fast Monte Carlo implementation. Often, Monte Carlo simulations are associated with offline analysis or verification, and are thought of as too slow for real-time usage. However, we describe an implementation that allows for fast computation and can be used in certain online applications. Examples are given for aircraft and automobile conflict detection. Both pseudo-code and Matlab code are provided to help explain and disseminate the approach.