Driver Reaction Time in Crash Avoidance Research: Validation of a Driving Simulator Study on a Test Track

McGehee, Daniel V.; Mazzae, Elizabeth N.; Baldwin, G H Scott

doi:10.1177/154193120004402026

Cited by 128 publications

(82 citation statements)

References 5 publications

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“…Therefore, there is a huge disparity in what is considered a "simulator." Subsequent examination of the data has shown that high fidelity simulators such as those at the University of Iowa, GE and others of similar complexity produce results that are very consistent with a "road" study, which is consistent with the findings of McGehee, Mazzae & Baldwin (2000).…”

Section: Discussionsupporting

confidence: 78%

Quantifying Driver Response Times Based upon Research and Real Life Data

Muttart

2005

Driving Assessment 2005 : Proceedings of the 3rd International Driving Symposium on Human Factors in Driver Assessment, Trainin

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Section: Discussionsupporting

confidence: 78%

Quantifying Driver Response Times Based upon Research and Real Life Data

Muttart

2005

Driving Assessment 2005 : Proceedings of the 3rd International Driving Symposium on Human Factors in Driver Assessment, Trainin

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“…Argument against human reaction time have been investigated and summarized in [7], and it is shown in their studies that reaction time to full braking are all around 2s obtained fro m regardless of simu lators or a test public road. The International Standards Organization [8] reports that maximu m permitted rate of decelerat ion is specified as 5m/s 2 , and longitudinal human-d riv ing braking is substantially less than the maximu m deceleration rate of 8.5m/s 2 even in the situation for crash avoidance or mitigation [9].…”

Section: Methodsmentioning

confidence: 99%

Analytical study of the lane-by-lane variation for lane-utility and traffic safety performance assessment using NGSIM data

2017

MATEC Web Conf.

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Abstract. Highlights the shortcoming of using a ggregate data as a whole with the assumption of similar lane -usepattern over lanes, traffic variations are attentively investigated to explore traffic dynamics distribution both in laneutility in efficiency and safety performance lane-by-lane. In this paper, above two dimensions are examined for each of five mainline lanes on Highway 101 in Angeles using Next Generation Simulation (NGSIM) data. T he lanespecific data is statistically processed to reflect lane utility in lane flow param eters along the location and identify Lane Risk Coefficient by verifying degree of consistence vehicles operation with 'Assured Clear Distance Ahead' criterion. It appears completely new that the most-middle lanes are similar utilized under congested conditions compared to two sides of the lanes, as well as the quite close values of L RC to reveals an uniformly safety level between Lane 2 and Lane 3. T he findings of this study are intended to be relevant with the calibration of microscopic simulation models and suggest implementing a proper variable speed towar d under-utilized shoulder Lane 5 but with worst safety performance.

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“…The latter time is below the American Association of State Highway and Transportation Officials' perception-brake response time of 2.5 s, which is used as an appropriate value for highway design. A study on crash avoidance was conducted in [35] comparing test track and simulator results for a sudden intersection crash scenario. The mean reaction time for releasing the throttle was 0.96 s in the simulator experiments and 1.28 s in the test track experiments.…”

Section: Reaction Timementioning

confidence: 99%

Fault classification method for the driving safety of electrified vehicles

Wanner

Drugge

Trigell

2014

Vehicle System Dynamics

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A fault classification method is proposed which has been applied to an electric vehicle. Potential faults in the different subsystems that can affect the vehicle directional stability were collected in a failure mode and effect analysis. Similar driveline faults were grouped together if they resembled each other with respect to their influence on the vehicle dynamic behaviour. The faults were physically modelled in a simulation environment before they were induced in a detailed vehicle model under normal driving conditions. A special focus was placed on faults in the driveline of electric vehicles employing in-wheel motors of the permanent magnet type. Several failures caused by mechanical and other faults were analysed as well. The fault classification method consists of a controllability ranking developed according to the functional safety standard ISO 26262. The controllability of a fault was determined with three parameters covering the influence of the longitudinal, lateral and yaw motion of the vehicle. The simulation results were analysed and the faults were classified according to their controllability using the proposed method. It was shown that the controllability decreased specifically with increasing lateral acceleration and increasing speed. The results for the electric driveline faults show that this trend cannot be generalised for all the faults, as the controllability deteriorated for some faults during manoeuvres with low lateral acceleration and low speed. The proposed method is generic and can be applied to various other types of road vehicles and faults.

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Driver Reaction Time in Crash Avoidance Research: Validation of a Driving Simulator Study on a Test Track

Cited by 128 publications

References 5 publications

Quantifying Driver Response Times Based upon Research and Real Life Data

Quantifying Driver Response Times Based upon Research and Real Life Data

Analytical study of the lane-by-lane variation for lane-utility and traffic safety performance assessment using NGSIM data

Fault classification method for the driving safety of electrified vehicles

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