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Gaze behaviour when approaching an intersection: Dwell time distribution and comparison with a quantitative prediction
- Source :
- Transportation Research : Part F, Traffic Psychology and Behaviour, Transportation Research : Part F, Traffic Psychology and Behaviour, 2015, 35 (35), pp 60-74. ⟨10.1016/j.trf.2015.10.015⟩, Transportation Research Part F: Traffic Psychology and Behaviour, Transportation Research Part F: Traffic Psychology and Behaviour, Elsevier, 2015, 35 (35), pp 60-74. ⟨10.1016/j.trf.2015.10.015⟩
- Publication Year :
- 2015
- Publisher :
- HAL CCSD, 2015.
-
Abstract
- The allocation of overt visual attention is investigated in a multi-task and dynamical situation: driving. The Expectancy-Value model of attention allocation stipulates that visual exploration depends on the expectancy and the value of the task-related information available in each Area Of Interest (AOI). We consider the approach to an intersection as a multi-task situation where two subtasks are involved: vehicle control and interactions with other drivers. Each of these subtasks is associated with some specific visual information present in the associated AOIs: the driver's lane and the intersecting road at the intersection. An experiment was conducted in a driving simulator, coupled with a head-mounted eye-tracker. The intersecting road's AOI's Expectancy was manipulated with the traffic density, and its Value was manipulated with the priority rule before the intersection (stop, yield, and priority). The distribution of visual attention and the dynamics of visual exploration were analyzed on 20 participants, taking into account the dwell time in the AOIs associated to the driving subtasks, and the gaze transitions between the AOIs. The results suggest that the visual attention to intersecting roads varied with the priority rule, and impacted the visual attention associated with the vehicle control subtask. In addition, a quantitative model was used to improve the understanding of the Expectancy and Value factors. The comparison of the data with the model's predictions enables quantifying the observed differences between the experimental factors. Finally, the results associated with the traffic density are discussed in relation to the nature of the relevant information while approaching the intersection.
- Subjects :
- EYE TRACKING
[SPI.OTHER]Engineering Sciences [physics]/Other
Engineering
Relation (database)
CARREFOUR
Poison control
Transportation
AREA OF INTEREST
Machine learning
computer.software_genre
Intersection
CONDUITE DU VEHICULE
VISUAL ATTENTION
Applied Psychology
Simulation
Civil and Structural Engineering
Expectancy theory
business.industry
Driving simulator
ATTENTION
Eye movement
Gaze
Dwell time
CONDUITE (VEH)
Automotive Engineering
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 13698478
- Database :
- OpenAIRE
- Journal :
- Transportation Research : Part F, Traffic Psychology and Behaviour, Transportation Research : Part F, Traffic Psychology and Behaviour, 2015, 35 (35), pp 60-74. ⟨10.1016/j.trf.2015.10.015⟩, Transportation Research Part F: Traffic Psychology and Behaviour, Transportation Research Part F: Traffic Psychology and Behaviour, Elsevier, 2015, 35 (35), pp 60-74. ⟨10.1016/j.trf.2015.10.015⟩
- Accession number :
- edsair.doi.dedup.....405129fcdde28531289df2027566a5b8