729 research outputs found
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A HMD-Based Virtual Reality Driving Simulator
Full text linkRecent advances in optics, HMD design, 3D graphics chips, and processes for personal computers have combined to make HMD based virtual reality driving simulators available at low cost. A HMD with a resolution of 1,024 by 768 with a FOV of 50o diagonally is now available for about 400. Personal computers can now support multiple processors that run over 1 Gigahertz. We discuss visual concerns with a HMD, choosing a HMD for a driving simulator, HMDs compared with fixed displays, consequences of improved frame rates, autonomous vehicles, and the use of a HMD based driving simulator for studying drivers who have cognitive impairments
Human Factors in Highway-Rail Crossing Accidents: The Influence of Driver Decision Style
Full text linkThis paper explores the hypothesis that driver decision-making style influences highway-rail crossing accidents. To investigate this, we have designed an analysis of variance experiment with three independent variables: “driver decision style,” “driver time pressure” and “intersection complexity.” To simulate the driving conditions, we identified and videotaped a number of dangerous crossings in downtown Los Angeles. The tapes represented different environmental complexities and time pressures a driver experiences while crossing an intersection. The tapes were played back to the subject drivers. The subjects were classified according to their decision styles. Dependent measures were designed based on a driver’s decision to cross the intersection. This paper presents the conceptual approach and the experimental design for this research
Cell Phone-Induced Perceptual Impairments During Simulated Driving
Full text linkOur research assessed the effects of cellular phone conversations on driving performance. When subjects were deeply involved in cellular phone conversations using either a hand-held or hands-free device, they were more than twice as likely to miss simulated traffic signals presented at the center of fixation than when they were not distracted by the cell phone conversation. By contrast, performance was not disrupted by listening to radio broadcasts or listening to a book on tape. One might argue that when subjects were conversing on a cell phone that they detected the simulated traffic signals, but that the responses to them were suppressed. To assess this, we examined the implicit perceptual memory for items that were presented at fixation but called for no response. Implicit perceptual memory was strong when subjects were not engaged in a cellphone conversation but impaired when they were so engaged. We suggest that active participation in a cell phone conversation disrupts performance by diverting attention to an engaging cognitive context other than the one immediately associated with driving
The Effect of a Vehicle Control Device on Driver Performance in a Simulated Tank Driving Task
Full text linkThe purpose of this study was to determine the effect of different vehicle controllers on driver performance in a simulated tank driving task. Eight male civilian volunteers with normal visual acuity drove a simulated tank on a digitized road terrain. The subject monitored his speed by means of a speedometer shown on the monitor. Independent variables were driving controller (joystick, or steering wheel with attached brake and accelerator pedal), and assigned driving speed of 15 or 45 mph (the maximum speed at which the subject was permitted to travel). Dependent variables were mean driving speed (the average speed at which the subject actually drove), and the proportion of time the center of the vehicle remained on the road during travel. Results indicated that subjects using the steering wheel obtained a significantly greater mean driving speed than those using the joystick only when they were permitted to drive a maximum speed of 45 mph. This difference may have little practical significance because the mean driving speed for the two controllers differed by less than 5 mph. There was no significant difference between controllers for the proportion of time the driver was able to keep the center of the vehicle on the road. Results implied that joystick controls have potential as an alternative control technology, and that the ergonomic placement of the joystick could be an important factor in enhancing driver performance
The Effects of Age and Distraction on Reaction Time in a Driving Simulator
Full text linkThe objective of this study was to investigate the effects of driver distraction – both cognitive and visual – on reaction time to unexpected road hazards. Participants operated a driving simulator while intermittently answering prerecorded questions of various difficulty (holding a “conversation” with the computer), or dialing specified numbers into a cellular telephone. Two road hazards were presented at unpredictable times and locations, including red brake lights and a red pedestrian-shape of approximately the same area as the brake lights. Targets were presented in two different locations: directly in front of the driver at the bottom of the screen, and off to the side of the road. The results showed a significant overall increase in reaction time for older subjects, as well as a strong interaction with the dialing task condition. There were no significant differences from the control for either easy or difficult verbal response conditions. In addition, stimuli on the side of the road took significantly longer to respond to, especially when combined with the dialing task. These data suggest a strong link between age, visual task load, stimulus location, and increased reaction time to unexpected stimuli
Driver Advocate™ Tool
No full textUsing scenario driven research, a Driver AdvocateTM (DA) [1] system has been designed to advise the driver about potentially unsafe situations based on information from environmental sensors [2]. DA is an intelligent dynamic system that monitors, senses, prioritizes, personalizes, and sends alerts to the driver appropriate to the moment. This has the potential to sharply decrease driver distraction and inattention. To support the realization of DA, a DA Tool (DAT) has been developed to coordinate with a KQ (previously Hyperion) virtual driving simulator and allow the merging of the simulated driving performance, the enviormental sensors, and the intelligent use of audio, visual, and tactile feedback to alert the driver to potential danger and unsafe driving behavior. DAT monitors the traffic, lane following, forward and side clearances, vehicle condition, cockpit distractions, Infotainment use, and the driver affective behavior. The DAT is designed to be highly configurable, flexible, and user friendly to facilitate creative freedom in designing usability and human factors experiments and rapid prototyping
Driver Alertness Detection Research Using Capacitive Sensor Array
Full text linkThe research project compared and analyzed physiological and performance data for 13 subjects driving a vehicle simulator. Each subject drove the simulator for morning, afternoon, and late night sessions. These sessions were intended to represent alertness conditions during an “awake” baseline period and the secondary and primary circadian sleep cycle periods. The sessions were approximately one hour, two hours, and two or three hours in length, respectively. With one exception, the subjects had experienced normal sleep the night before the test. Five men and eight women participated, ranging in age from 25 to 59. Physiological data included: real-time PERCLOS (percentage of slow-eye closure over one minute) using an infrared-reflective camera; head position coordinates using an overhead capacitive sensor array; and video of the right front of the subject’s face. Performance data included: vehicle speed, lane departures, lane deviation, and steering/turn signal data. The research manager maintained logs of unusual circumstances such as departing the roadway, falling asleep at the wheel, excessive speeding, etc. Head position data was analyzed and compared to the videos. A multi-element algorithm was developed which captured patterns of head motion found to be characteristic of drowsiness. The algorithm output was compared to roadway departures noted in the research manager’s logs of unusual events. The comparison showed a capability of advance detection of about 87% of driver roadway departures with a false positive rate of about 15%
Evaluation of Driving-Assistance Systems Based on Drivers' Workload
Full text linkThis paper describes an experimental study concerning an evaluation of advanced driving-assistance systems using methods for estimating workload levels. The effects of such systems on drivers’ mental workload and driving performance were measured experimentally using the driving simulator. Six subjects were instructed to drive the simulator in a highway environment with and without Adaptive Cruise Control (ACC) and/or the collision-warning system (CWS). To assess the effectiveness of these systems on drivers’ performance, the subjects were asked to calculate sums of single- or double-digit figures displayed. The results show that higher accuracy was obtained under a condition with ACC than without it. To estimate the subjects’ mental workload levels, their electrocardiograms and respiration data were recorded during the sessions and the RRI, heart rate variance and respiration frequency were calculated. The results indicate that the provision of the CWS and ACC reduced the subjects’ mental workload compared with the situation without the systems
BEI's Driver Skill Enhancement Program (D-SEP): Brief Review of Experimental Mini-Program and Conclusions
No full textThe program had its beginnings in a local group meeting to provide input to the “White House Commission on Aging”, about 6 years ago. At this meeting, the author became acutely aware of the problems older people had with driving. Building on the author’s twenty-five years of activities with “Drivers Education” programs on high-speed road tracks, Ref. 1, he started a research program, which continues today. Research was initiated into other driving schools and their methods, and study of the fundamental elements of driving (from many sources). The research was supplemented by data gathering on the “process of driving” (by discussions with many “experts”). There is general agreement that driving is a combination of several skills; and there are three basic elements of driving: a) information gathering, primarily visual b) cognitive processing, during which the large amount of data obtained visually is sifted to separate out what is crucial for the driving experience. A decision is made as to what should be done. c) physical activities of the arms and legs, to carry out the decisions reached in the cognitive process. This process is repeated continuously as one drives, since driving is a “dynamic” process. The BEI program is based on two premises: 1) P+A=a good driver. P is Preparation: what the drivers, in their cars, can actually do. A is Anticipation: the visual-cognitive process which buys time to carry out the physical activities involved in making a car perform. Anticipation is usually not consciously practiced, although carried out in some form, for all driving. 2) “Training and practice” will, in most cases, considerably enhance the skills required for driving