| People | Locations | Statistics |
|---|---|---|
| Ziakopoulos, Apostolos | Athens |
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| Vigliani, Alessandro | Turin |
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| Catani, Jacopo | Rome |
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| Statheros, Thomas | Stevenage |
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| Utriainen, Roni | Tampere |
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| Guglieri, Giorgio | Turin |
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| Martínez Sánchez, Joaquín |
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| Tobolar, Jakub |
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| Volodarets, M. |
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| Piwowar, Piotr |
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| Tennoy, Aud | Oslo |
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| Matos, Ana Rita |
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| Cicevic, Svetlana |
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| Sommer, Carsten | Kassel |
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| Liu, Meiqi |
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| Pirdavani, Ali | Hasselt |
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| Niklaß, Malte |
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| Lima, Pedro | Braga |
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| Turunen, Anu W. |
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| Antunes, Carlos Henggeler |
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| Krasnov, Oleg A. |
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| Lopes, Joao P. |
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| Turan, Osman |
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| Lučanin, Vojkan | Belgrade |
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| Tanaskovic, Jovan |
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Pekkanen, Jami Joonas Olavi
University of Helsinki
in Cooperation with on an Cooperation-Score of 37%
Topics
- autonomous vehicle
- road
- highway traffic
- variable
- behavior
- decision making
- pedestrian
- laboratory
- human being
- traffic safety
- signalling
- yielding
- deceleration
- psychology
- driver
- driving
- autonomous driving
- supervisor
- eye
- automobile
- algorithm
- simulation
- regression analysis
- travel
- steering
- sampling
- eye movement
- tangent
- industry
- planning
- driving simulator
- monitoring
- headway
- crossheading
- submarine
- geometry
- fee
- coordination
- humanities
- poison
- automobile driver
- hazard
- automation
- eccentricity
- health
- gender
- offender
- modeling
- crash
- attention
- longitudinal control
- vision
- data file
- experiment
- engineering
- virtual reality
- brake
- car following
- uncertainty
- instrumented vehicle
- state of the art
- visual perception
- validation
- transportation engineering
- indicating instrument
- acceleration
- employed
- microsimulation
- traffic simulation
- traffic psychology
- driving behavior
- transient
- distraction
- interface
- aggregate
- traffic engineering
- human factor
- data
- Statistic
- age
- passenger
- vehicle occupant
- child
- adolescent
- sleep
- male
- crash investigation
- adult
- female
- parent
- speeding
- quantitative analysis
- region
- pavement
- steady state
- show 65 more
Publications (14/14 displayed)
- 2022Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisionscitations
- 2021Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisionscitations
- 2021Drivers use active gaze to monitor waypoints during automated drivingcitations
- 2020Drivers use active gaze to monitor waypoints during automated driving
- 2020Humans use Optokinetic Eye Movements to Track Waypoints for Steeringcitations
- 2019Looking at the Road When Driving Around Bends : Influence of Vehicle Automation and Speedcitations
- 2019Table_1_Looking at the Road When Driving Around Bends: Influence of Vehicle Automation and Speed.DOCX
- 2018A computational model for driver's cognitive state, visual perception and intermittent attention in a distracted car following taskcitations
- 2017Task-Difficulty Homeostasis in Car Following Modelscitations
- 2017Task-Difficulty Homeostasis in Car Following Models: Experimental Validation Using Self-Paced Visual Occlusioncitations
- 2017Trade-off between jerk and time headway as an indicator of driving stylecitations
- 2017Task-Difficulty Homeostasis in Car Following Models : Experimental Validation Using Self-Paced Visual Occlusioncitations
- 2016Child passengers and driver culpability in fatal crashes by driver gendercitations
- 2013Pursuit Eye-Movements in Curve Driving Differentiate between Future Path and Tangent Point Modelscitations
Places of action
article
Task-Difficulty Homeostasis in Car Following Models : Experimental Validation Using Self-Paced Visual Occlusion
Abstract
Car following (CF) models used in traffic engineering are often criticized for not incorporating “human factors” well known to affect driving. Some recent work has addressed this by augmenting the CF models with the Task-Capability Interface (TCI) model, by dynamically changing driving parameters as function of driver capability. We examined assumptions of these models experimentally using a self-paced visual occlusion paradigm in a simulated car following task. The results show strong, approximately one-to-one, correspondence between occlusion duration and increase in time headway. The correspondence was found between subjects and within subjects, on aggregate and individual sample level. The long time scale aggregate results support TCI-CF models that assume a linear increase in time headway in response to increased distraction. The short time scale individual sample level results suggest that drivers also adapt their visual sampling in response to transient changes in time headway, a mechanism which isn’t incorporated in the current models. ; Peer reviewed
Topics
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