Visual Information for Car Following by Drivers: Role of Scene Information

Sauer

2007

Hum Factors

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Section: Discussionmentioning

confidence: 88%

Optical Information for Car Following: The Driving by Visual Angle (DVA) Model

Sauer

2007

Hum Factors

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“…Instead, distance and speed information is based on the driver's perception of distance and speed and thus dependent on visual information for distance and speed. Recently, Andersen and Sauer have proposed and tested a model [driving by visual angle (DVA) model] for car following based on visual information for distance and speed (3,4). Specifically, their model uses visual angle and change in visual angle as the primary source of information for maintaining distance and for detecting distance that was well within the range of visibility.…”

Section: Driving By Visual Anglementioning

confidence: 99%

Effects of Reduced Visibility from Fog on Car-Following Performance

Kang

Transportation Research Record

2008

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A study examined the effects of reduced visibility of scene information because of fog on car-following performance. Drivers were presented with a straight roadway scene in a driving simulator and were asked to maintain a predetermined driving distance in response to speed variations of a lead vehicle. Lead vehicle speed varied according to a sum of three prime sine wave frequencies. Five simulated fog density conditions and three average lead vehicle velocities were examined. Car-following performance was assessed using distance headway, variance of distance headway, root-mean-square (RMS) velocity error, control gain, phase angle, and squared coherence. Distance headway decreased only at the highest fog density condition examined. RMS velocity error increased with an increase in fog density. These results indicate that drivers had greater difficulty responding to changes in lead vehicle speed than to changes in headway. Results for squared coherence indicated that the effects of fog were greatest for the highest rate of change in lead vehicle speed (i.e., highest frequency examined). The importance of visual factors for optimal car-following performance is discussed.

“…The results of their study indicate that the DVA model, as compared to other car following models based on precise headway distance and LV speed, could better predict driver performance in both simulator and real world driving conditions. In a related study Andersen and Sauer (2005) showed that information of the driving scene was also used in car following. They presented drivers with an active car following task in which LV speed varied according to a complex waveform.…”

mentioning

confidence: 99%

Age-related declines in car following performance under simulated fog conditions

Kang

Accident Analysis & Prevention

2010

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The present study examined age-related differences in car following performance when contrast of the driving scene was reduced by simulated fog. Older (mean age of 72.6) and younger (mean age of 21.1) drivers were presented with a car following scenario in a simulator in which a lead vehicle (LV) varied speed according to a sum of three sine wave functions. Drivers were shown an initial following distance of 18m and were asked to maintain headway distance by controlling speed to match changes in LV speed. Five simulated fog conditions were examined ranging from a no fog condition (contrast of 0.55) to a high fog condition (contrast of 0.03). Average LV speed varied across trials (40, 60, or 80 km/h). The results indicated age-related declines in car following performance for both headway distance and RMS (root mean square) error in matching speed. The greatest decline occurred at moderate speeds under the highest fog density condition, with older drivers maintaining a headway distance that was 21% closer than younger drivers. At higher speeds older drivers maintained a greater headway distance than younger drivers. These results suggest that older drivers may be at greater risk for a collision under high fog density and moderate speeds. KeywordsAging; driving; car following; fog; driving safety Driving simulators allow for the investigation and study of driving situations that are a potential safety risk to the driver. For example, driving simulation studies have examined the ability of drivers to detect impending collisions. Such an issue cannot be studied under real world conditions because of the potential accident risk to the driver should a collision occur. Thus, an important benefit of driving simulation studies is that it allows researchers to understand the perceptual or cognitive limitations of the driver by examining conditions that under real world driving that would introduce risk to the driver. The present study examined a driving scenario that is likely to introduce considerable risk to the driver under real world conditions---older drivers performing a car following task under foggy conditions. An important and consistent finding regarding driving safety is that accident risk increases for older driver populations (Langford & Koppel, 2006;Evans, 2004;Owsley et al., 1991). A detailed analysis of this issue was presented by Evans (2004) Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. FARS (Fatality Analysis Reporting System). The results indicated a steady increase in accident fatalities and rate of severe crashes for older drivers beginning at age 6...