The interaction between automated vehicles (AVs) and vulnerable road users is increasingly important since the adoption of AVs is closer to reality. Particularly, the pedestrians' crossing behaviour are extremely complex, and it is difficult for AVs to predict pedestrians' decisions and motion behaviour. One of the important problems is how to characterize pedestrians crossing willingness (PCW), which is important for AV systems. Currently, few models have been proposed to characterize PCW. The most relevant models, pedestrian gap acceptance models, are mostly pure statistical approaches which are difficult to apply to a wide range of scenarios. In this paper, to avoid these drawbacks, we developed a novel PCW model by employing a continuously changing psychophysical stimulus, looming, which characterizes the visual information of approaching vehicles through the kinematics model of crossing scenario. In addition, a perception threshold is introduced to constrain the model. Results in this study showed that the PCW model can accurately capture the effects of the vehicle speed, distance and size on pedestrians' behaviour pattern. It was also found that pedestrians have maximum willingness to cross the street when this stimulus is beyond the perception threshold. We found that the model fit well with data collected from previous gap acceptance studies.
|Title of host publication||2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC 2020): Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 24 Dec 2020|
|Event||23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 - Rhodes, Greece|
Duration: 20 Sep 2020 → 23 Sep 2020
|Name||2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020|
|Conference||23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020|
|Period||20/09/2020 → 23/09/2020|
Bibliographical notePublisher Copyright:
© 2020 IEEE.
Copyright 2021 Elsevier B.V., All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence
- Decision Sciences (miscellaneous)
- Information Systems and Management
- Modelling and Simulation