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National Advanced Driving Simulator

About: National Advanced Driving Simulator is a research topic. Over the lifetime, 90 publications have been published within this topic receiving 1419 citations.


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04 Jun 2001
TL;DR: The National Highway Traffic Safety Administration (NHTSA) identified driver distraction as a high-priority topic by the National Automated Driving Test Center (NADT) as mentioned in this paper, and developed methods to assess the extent to which in-vehicle technologies may contribute to crashes.
Abstract: Driver distraction has been identified as a high-priority topic by the National Highway Traffic Safety Administration, reflecting concerns about the compatibility of certain in-vehicle technologies with the driving task, whether drivers are making potentially dangerous decisions about when to interact with in-vehicle technologies while driving, and that these trends may accelerate as new technologies continue to become available. Since 1991, NHTSA has conducted research to understand the factors that contribute to driver distraction and to develop methods to assess the extent to which in-vehicle technologies may contribute to crashes. This paper summarizes significant findings from past NHTSA research in the area of driver distraction and workload, provides an overview of current ongoing research, and describes upcoming research that will be conducted, including research using the National Advanced Driving Simulator and work to be conducted at NHTSA's Vehicle Research and Test Center. Preliminary results of the ongoing research are also presented. For the covering abstract see ITRD E111577.

300 citations

Journal ArticleDOI
TL;DR: In this paper, the effectiveness of various warning modalities for reengaging distracted drivers during severe braking situations that exceed adaptive cruise control (ACC) capability was examined. And they found that drivers experienced two severe, four moderate, and eight mild braking situations without driver intervention.
Abstract: Adaptive cruise control (ACC) is a rapidly emerging in-vehicle technology that can enhance or degrade driving safety. A critical factor governing the safety benefit of ACC concerns the driver's ability to assume control of the vehicle in situations that exceed ACC capabilities. The effectiveness of various warning modalities for reengaging distracted drivers during severe braking situations that exceed ACC capability was examined. Warnings that paired a visual icon with sound, seat vibration, or brake pulsation or that combined all three cues were compared. A total of 60 participants drove for 35 min in the National Advanced Driving Simulator. Drivers experienced two severe, four moderate, and eight mild braking situations. ACC could accommodate all but the two severe situations without driver intervention. It also provided a substantial benefit during mild events of lead vehicle braking, enabling drivers to maintain a longer, more consistent minimum time to collision. Unlike performance in previous studi...

72 citations

Journal ArticleDOI
01 Sep 2012
TL;DR: The results show that steering-angle can be used to predict drowsiness related lane-departures six seconds before they occur, and suggest that the random forest algorithm, when paired with an alert system, could significantly reduce vehicle crashes.
Abstract: Drowsy driving is a significant factor in many motor vehicle crashes in the United States and across the world. Efforts to reduce these crashes have developed numerous algorithms to detect both acute and chronic drowsiness. These algorithms employ behavioral and physiological data, and have used different machine learning techniques. This work proposes a new approach for detecting drowsiness related lane departures, which uses unfiltered steering wheel angle data and a random forest algorithm. Using a data set from the National Advanced Driving Simulator the algorithm was compared with a commonly used algorithm, PERCLOS and a simpler algorithm constructed from distribution parameters. The random forest algorithm had higher accuracy and Area Under the receiver operating characteristic Curve (AUC) than PERCLOS and had comparable positive predictive value. The results show that steering-angle can be used to predict drowsiness related lane-departures six seconds before they occur, and suggest that the random forest algorithm, when paired with an alert system, could significantly reduce vehicle crashes. Language: en

71 citations

Journal ArticleDOI
TL;DR: This algorithm has a significantly lower false positive rate than PERCLOS-the current gold standard-and baseline, non-contextual, algorithms under design parameters that prioritize drowsiness detection and suggests contextual factors should be considered in subsequent algorithm development processes.

67 citations

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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20211
20181
20174
20162
20151
20141