Processing of eye/head-tracking data in large-scale naturalistic driving data sets

Publisher's full text
Trent Victor
Claudia Wege
Erik Steinmetz

Driver distraction and driver inattention are frequently recognized as leading causes of crashes and incidents. Despite this fact, there are few methods available for the automatic detection of driver distraction. Eye tracking has come forward as the most promising detection technology, but the technique suffers from quality issues when used in the field over an extended period of time. Eye-tracking data acquired in the field clearly differs from what is acquired in a laboratory setting or a driving simulator, and algorithms that have been developed in these settings are often unable to operate on noisy field data. The aim of this paper is to develop algorithms for quality handling and signal enhancement of naturalistic eye- and head-tracking data within the setting of visual driver distraction. In particular, practical issues are highlighted. Developed algorithms are evaluated on large-scale field operational test data acquired in the Sweden-Michigan Field Operational Test (SeMiFOT) project, including data from 44 unique drivers and more than 10000 trips from 13 eye-tracker-equipped vehicles. Results indicate that, by applying advanced data-processing methods, sensitivity and specificity of eyes-off-road glance detection can be increased by about 10%. In conclusion, postenhancement and quality handling is critical when analyzing large databases with naturalistic eye-tracking data. The presented algorithms provide the first holistic approach to accomplish this task. © 2011 IEEE.

MEET US


7
Dec

Shipping and the environment – research meets reality

Centre for Transport Studies (CTS) in co-operation with Ports of Stockholm invite you to the seminar Shipping and the environment – research meets reality.

LATEST NEWS


2017-10-26

Simulation of cut-in by manually driven vehicles in platooning scenarios

A study in a VTI-driving simulator has showed that a platoon will be able to handle a cut in from a manually driven car. The results of this study have recently been presented at two conferences in Japan.


2017-10-16

ERPUG Forum

The five-year anniversary of European Road Profile Users' Group (ERPUG) Forum will take place at Ramboll head quarter, Copenhagen, Denmark October 19-20, 2017.


2017-09-29

Self-driving buses in Sweden next year?

A self-driving, fossil-free bus. This idea might become reality through a forthcoming collaborative project involving the Swedish National Road and Transport Research Institute (VTI), Linköping University and several other participants. The project group aim...


2017-09-29

New climate-proof solutions for hard surfaces in cities

High-density road infrastructure that emphasise maximum durability and minimum maintenance create inflexible systems, which put increased stress on urban trees and lead to increased risk for flooding. Over the past five years, the ‘Climate-proof solutions for...


2017-09-29

VTI is preparing for automated vehicles

Automation of traffic systems will lead to major changes. The European Union’s (EU) CoEXist research project began in June 2017 with the aim of preparing cities and road operators for the introduction of self-driving vehicles. The Swedish National Road and...


2017-07-05

Vehicle Driver Monitoring: sleepiness and cognitive load

To prevent road crashes it is important to understand driver related contributing factors, which have been suggested to be the critical reason in 94 per cent of crashes. The overall aim of the project Vehicle Driver Monitoring has been to advance the...