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- Ahlström, Christer, et al.
(författare)
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Processing of Eye/Head-Tracking Data in Large-Scale Naturalistic Driving Data Sets
- 2012
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; vol.13:no.2, s. pp.553-564
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Tidskriftsartikel (refereegranskat)abstract
- 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 10 000 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.
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| 2. |
- Engström, Johan A Skifs, 1973, et al.
(författare)
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Attention selection and multitasking in everyday driving: A conceptual model
- 2013
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Ingår i: Driver Distraction and Inattention: Advances in Research and Countermeasures. - 9781409425854 ; , s. 27-54
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter outlines a conceptual model of attention selection and multitasking in everyday driving. While existing theoretical and empirical work on attention in driving has mainly focused on dual-task interference in experimental settings, the present model aims to account for attention selection in natural driving situations. The model starts from the view of attention as a form of adaptive behaviour and emphasises the key role of expectancy, the dynamic interplay between top-down and bottom-up selection, the often habitual nature of attention selection in real driving and how attention selection is driven by perceived and expected value. However, the model also offers a novel characterisation of dual-task interference mechanisms and more precise definitions of key concepts such as driver inattention and driver distraction. Based on the model, a general conceptualisation of the relation between attention selection and crash causation is proposed and implications for the design of driver support systems and automotive human-machine interfaces are discussed.
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| 3. |
- Victor, Trent, 1968, et al.
(författare)
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Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk
- 2014
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This work was sponsored by the secondStrategic Highway Research Program (SHRP 2), which isadministered by the Transportation Research Board of the National Academies. This project wasmanaged by Ken Campbell, Chief Program Officer for SHRP 2 Safety, and Jim Hedlund,SHRP 2SafetyCoordinator.The research reported on herein was performed by the main contractor SAFER Vehicle and TrafficSafety Centre at Chalmers, Gothenburg, Sweden. SAFER is a joint research unit where 25 partnersfrom the Swedish automotive industry, academia and authoritiescooperate to make a center ofexcellence within the field of vehicle and traffic safety (seewww.chalmers.se/safer). The host andlegal entity SAFER is Chalmers University of Technology. Principle Investigator Trent Victor is AdjunctProfessor at Chalmers and worked on the project as borrowed personnel to Chalmers but his mainemployer is Volvo Cars. The other authors of this report are Co-PI Marco Dozza, Jonas Bärgman, andChristian-Nils Boda of Chalmers University of Technology(as a SAFER partner); Johan EngströmandGustav Markkulaof Volvo Group Trucks Technology(as a SAFER partner); John D. Lee of Universityof Wisconsin-Madison (as a consultant to SAFER); and Carol Flannagan of University of MichiganTransportation Research Institute (UMTRI) (as a consultant to SAFER). The authors acknowledge thecontributions to this research from Ines Heinig, Vera Lisovskaja, Olle Nerman, Holger Rootzén,Dmitrii Zholud, Helena Gellerman, Leyla Vujić, Martin Rensfeldt,Stefan Venbrant, Akhil Krishnan,Bharat Mohan Redrouthu, Daniel Nilssonof Chalmers; Mikael Ljung-Aust of Volvo Cars; Erwin Boer;Christer Ahlström and Omar Bagdadi of VTI.
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| 5. |
- Victor, Trent, 1968, et al.
(författare)
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Safer Glances, Driver Inattention, and Crash Risk: An Investigation Using the SHRP 2 Naturalistic Driving Study
- 2013
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Ingår i: Proceedings of the 3rd Conference of Driver Distraction and Inattention, Gothenbrug, 4-6 September, 2013.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents progress in the SHRP2 Safer Glances analysis project which aims to establish the relationship between crash risk and driver inattention using rear-end crashes and near crashes. Results will pinpoint the most dangerous glances away from the road and show how to change glance behavior to be safer. The Second Strategic Highway Research Program (SHRP2) is conducting the largest and most comprehensive naturalistic driving study (NDS) ever conducted (see Campbell, 2013; and www.shrp2nds.us). The study has recruited 2,800 volunteer drivers. All of their trips are recorded for one to two years. When complete in early 2014, the NDS data set will contain over 33,000,000 travel miles from over 3,800 vehicle-years of driving – over 4 petabytes of data. The analysis plan is formulated in five analytic steps. Each analytic step is expected to provide better precision and explore different components of the inattention-risk relationship by providing more detail on inattention-risk relationships under different circumstances – relationships to timing with optical parameters, glance characteristics, and relationships with respect to different levels of crash severity. This research will identify a more precise relationship between glance patterns and their associated risk around a sweet spot, a time when perceptual information is particularly valuable in crash avoidance. Further, it will relate glance behavior to injury severity as defined by new severity scales. This set of functions will indicate crash likelihood and/or injury severity for certain contextual characteristics of the lead-vehicle crash scenario, such as traffic density, road type, and speed. These relationships can be used to show more precisely which glance behaviors are safer than others. Safer glance strategies for interacting with electronics and the traffic environment can be encouraged in a number of ways including design guidelines, education, and in-vehicle feedback. Likewise, the most dangerous glances can be pinpointed and associated with improvements to appropriate countermeasures like distraction guideline performance criteria and active safety system technology.
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