Driver Glance Behaviour in Intersection Crashes: A SHRP2 Naturalistic Data Analysis

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/256454
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Type: Examensarbete för masterexamen
Master Thesis
Title: Driver Glance Behaviour in Intersection Crashes: A SHRP2 Naturalistic Data Analysis
Authors: Chandran, Tejas
Abstract: Road traffic crashes are one of the biggest problems in modern history. Over recent years, several attempts have been made to gain an in-depth understanding of crashes and their causation. The introduction of unobtrusive data collection methods in the form of naturalistic driving studies has given traffic safety researchers an unprecedented level of insight into the vehicle and driver during these safety critical situations. This research was started with the aim of utilising this data from naturalistic driving studies to reconstruct crashes in intersections to gain a better understanding of the driver’s gaze behaviour. This was done by first developing a toolbox within the software environment of MATLAB. The toolbox used the vehicle kinematic data from the naturalistic driving study SHRP2 or the Second Strategic Highway Research Project. The toolbox was designed to transform the driver’s gaze from vehicle coordinates to intersection coordinates. The target vehicle was approximated into the simulation environment with the help of the video data and manual annotation. Factors such the driver’s gaze directions, eyes on target and intersection gaze timings were obtained. Well known psychological models such as the Situational Awareness, SEEV model and the more recent Predictive Processing model were evaluated to understand the findings. The results of the research showed that drivers in most of the events analysed had possibly seen and continued tracking the threat from the theoretical point of no return until the crash itself. These results were intriguing in that the drivers were not observed to engage in evasive manoeuvres until too late. A hypothesis was developed with the help of the predictive processing model to understand and explain this behaviour.
Keywords: Transport;Människa-datorinteraktion (interaktionsdesign);Farkostteknik;Transport;Human Computer Interaction;Vehicle Engineering
Issue Date: 2018
Publisher: Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper
Chalmers University of Technology / Department of Mechanics and Maritime Sciences
Series/Report no.: Master's thesis - Department of Mechanics and Maritime Sciences : 2018:95
URI: https://hdl.handle.net/20.500.12380/256454
Collection:Examensarbeten för masterexamen // Master Theses



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