Max Theisen , Caroline Schießl , Wolfgang Einhäuser , Gustav Markkula
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引用次数: 0
Abstract
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.
期刊介绍:
The International Journal of Human-Computer Studies publishes original research over the whole spectrum of work relevant to the theory and practice of innovative interactive systems. The journal is inherently interdisciplinary, covering research in computing, artificial intelligence, psychology, linguistics, communication, design, engineering, and social organization, which is relevant to the design, analysis, evaluation and application of innovative interactive systems. Papers at the boundaries of these disciplines are especially welcome, as it is our view that interdisciplinary approaches are needed for producing theoretical insights in this complex area and for effective deployment of innovative technologies in concrete user communities.
Research areas relevant to the journal include, but are not limited to:
• Innovative interaction techniques
• Multimodal interaction
• Speech interaction
• Graphic interaction
• Natural language interaction
• Interaction in mobile and embedded systems
• Interface design and evaluation methodologies
• Design and evaluation of innovative interactive systems
• User interface prototyping and management systems
• Ubiquitous computing
• Wearable computers
• Pervasive computing
• Affective computing
• Empirical studies of user behaviour
• Empirical studies of programming and software engineering
• Computer supported cooperative work
• Computer mediated communication
• Virtual reality
• Mixed and augmented Reality
• Intelligent user interfaces
• Presence
...