| 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
Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisions
Abstract
Human behavior and interaction in road traffic is highly complex, with many open scientific questions of high applied importance, not least in relation to recent development efforts toward automated vehicles. In parallel, recent decades have seen major advances in cognitive neuroscience models of human decision-making, but these models have mainly been applied to simplified laboratory tasks. Here, we demonstrate how variable-drift extensions of drift diffusion (or evidence accumulation) models of decision-making can be adapted to the mundane yet non-trivial scenario of a pedestrian deciding if and when to cross a road with oncoming vehicle traffic. Our variable-drift diffusion models provide a mechanistic account of pedestrian road-crossing decisions, and how these are impacted by a variety of sensory cues: time and distance gaps in oncoming vehicle traffic, vehicle deceleration implicitly signaling intent to yield, as well as explicit communication of such yielding intentions. We conclude that variable-drift diffusion models not only hold great promise as mechanistic models of complex real-world decisions, but that they can also serve as applied tools for improving road traffic safety and efficiency.
Topics
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