DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Pedestrians' road-crossing decisions: Comparing different drift-diffusion models

Theisen, Max and Schießl, Caroline and Einhäuser, Wolfgang and Markkula, Gustav (2023) Pedestrians' road-crossing decisions: Comparing different drift-diffusion models. International Journal of Human-Computer Studies, 183. Elsevier. doi: 10.1016/j.ijhcs.2023.103200. ISSN 1071-5819.

[img] PDF - Published version

Official URL: https://www.sciencedirect.com/science/article/pii/S1071581923002094


The decision of whether to cross a road or wait for a car to pass, humans make frequently and effortlessly. Recently, the application of drift-diffusion models (DDMs) on pedestrians' decision-making has proven useful in modelling crossing behaviour in pedestrian-vehicle interactions. These models consider binary decision-making as an incremental accumulation of noisy evidence over time until one of two choice thresholds (to cross or not) is reached. One open question is whether the assumption of a kinematics-dependent drift-diffusion process, which was made in previous pedestrian crossing DDMs, is justified, with DDM-parameters varying over time according to the developing traffic situation. It is currently unknown whether kinematics-dependent DDMs provide a better model fit than conventional DDMs, which are fitted per condition. Furthermore, previous DDMs have not considered reaction times for the not-crossing option. We address these issues by a novel experimental design combined with modelling. Experimentally, we use a 2-alternative-forced-choice paradigm, where participants view videos of approaching cars from a pedestrian's perspective and respond whether they want to cross before the car or to wait until the car has passed. Using these data, we perform thorough model comparison between kinematics-dependent and condition-wise fitted DDMs. Our results demonstrate that condition-wise fitted DDMs can show better model fits than kinematics-dependent DDMs as reflected in the mean-squared-errors. The condition-wise fitted models need considerably more parameters, but in some cases still outperform kinematics-dependent DDMs in measures that penalize the parameter number (e.g., Akaike information criterion). Introducing a starting point bias provides support for the novel hypothesis of rapid early evidence build-up from the initial view of the vehicle distance. The drift rates obtained for the condition-wise fitted models align with the assumptions in the kinematics-dependent models, confirming that pedestrians' decision processes are kinematics-dependent. However, the partial preference for condition-wise fitted models in the model selection suggests that the correct form of kinematics-dependence has not yet been identified for all DDM-parameters, indicating room for improvement of current pedestrian crossing DDMs. Developing more accurate models of human cognitive processes will likely facilitate autonomous vehicles to understand pedestrians' intentions as well as to show unambiguous human-like behaviour in future traffic interactions with humans.

Item URL in elib:https://elib.dlr.de/200678/
Document Type:Article
Title:Pedestrians' road-crossing decisions: Comparing different drift-diffusion models
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Schießl, CarolineUNSPECIFIEDhttps://orcid.org/0000-0001-5849-5075UNSPECIFIED
Einhäuser, WolfgangUNSPECIFIEDhttps://orcid.org/0000-0001-7516-9589UNSPECIFIED
Date:30 November 2023
Journal or Publication Title:International Journal of Human-Computer Studies
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Series Name:Computational Models of Human-Automated Vehicle Interaction
Keywords:Cognitive modelling; Decision-making; Drift-diffusion model; Pedestrian–vehicle interaction; Pedestrian crossing
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - KoKoVI - Koordinierter kooperativer Verkehr mit verteilter, lernender Intelligenz
Location: Braunschweig
Institutes and Institutions:Institute of Transportation Systems > Information Gathering and Modelling, BS
Institute of Transportation Systems > Information Flow Modelling in Mobility Systems, BS
Deposited By: Theisen, Max
Deposited On:11 Dec 2023 12:20
Last Modified:15 Jan 2024 12:43

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.