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Using manual measurements on event recorder video and image processing algorithms to extract optical parameters.

Jonas Bärgman (Institutionen för tillämpad mekanik, Fordonssäkerhet) ; Julia Werneke (Institutionen för tillämpad mekanik, Fordonssäkerhet) ; Christian-Nils Boda (Institutionen för tillämpad mekanik, Fordonssäkerhet) ; Johan Engström (Institutionen för tillämpad mekanik, Fordonssäkerhet) ; Kip Smith (Institutionen för tillämpad mekanik, Fordonssäkerhet)
Proceedings of the Seventh International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design p. 177-183. (2013)
[Konferensbidrag, refereegranskat]

Vehicle kinematics and optical parameters such as optical angle, optical expansion rate, and tau are thought to underlie drivers’ ability to avoid and handle critical traffic situations. Analyses of these parameters in naturalistic driving data with video, such as commercial event recordings of near-crashes and crashes, can provide insight into driver behavior in critical traffic situations. This paper describes a pair of methods, one for the range to a lead vehicle and one for its optical angle, that are derived from image processing mathematics and that provide driver behavior researchers with a relatively simple way to extract optical parameters from video-based naturalistic data when automatic image processing is not possible. The methods begin with manual measurements of the size of other road users on a video on a screen. To develop the methods, 20 participants manually measured the width of a lead vehicle on 14 images where the lead vehicle was placed at different distances from the camera. An on-market DriveCam Event Recorder was used to capture these images. A linear model that corrects distortion and modeling optics was developed to transform the on-screen measurements distance (range) to and optical angle of the vehicle. The width of the confidence interval for predicted range is less than 0.1m when the actual distance is less than 10m and the lead-vehicle width estimate is correct. The methods enable driver behavior researchers to easily and accurately estimate useful kinematic and optical parameters from videos (e.g., of crashes and near-crashes) in event-based naturalistic driving data.



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Denna post skapades 2013-12-10. Senast ändrad 2015-12-17.
CPL Pubid: 188852

 

Institutioner (Chalmers)

Institutionen för tillämpad mekanik, Fordonssäkerhet (2005-2017)

Ämnesområden

Transport
Teknisk mekanik
Tillämpad psykologi

Chalmers infrastruktur

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Denna publikation ingår i:


On the analysis of naturalistic driving data


Methods for Analysis of Naturalistic Driving Data in Driver Behavior Research