Back to Search
Start Over
Game-Based Simulation and Study of Pedestrian-Automated Vehicle Interactions
- Source :
- Automation, Vol 3, Iss 3, Pp 315-336 (2022)
- Publication Year :
- 2022
- Publisher :
- MDPI AG, 2022.
-
Abstract
- We identify the need for enhanced pedestrian–vehicle simulation tools and build such a tool to explore the interaction among pedestrian “players” and virtual human- and automated-vehicles for different scenarios taking place in an urban environment. We first present contemporary research tools and then propose the design and development of a new desktop application that facilitates pedestrian-point-of-view research. We then conduct a three-step user experience experiment, in which a small number of participants answer questions before and after using the application to interact with virtual human and automated vehicles in diverse road-crossing scenarios. Behavioral results observed in virtuality, especially when motivated by consequence, tend to simulate real life sufficiently well to inform design choices. From the simulation, we observed valuable insights into human–vehicle interactions. Upon completing this preliminary testing, we iterated the tool’s design and ultimately conducted an 89-participant study of human–vehicle interactions for three scenarios taking place in a virtual environment. Our tool raised participant awareness of autonomous vehicles and their capabilities and limitations, which is an important step in overcoming public distrust of AVs. We additionally saw that participants trust humans and technology less as drivers than in other contexts, and that pedestrians feel safer around vehicles with autonomy indicators. Further, we note that study participants increasingly feel safe with automated vehicles with increased exposure. These preliminary results, as well as the efficacy of the tool’s design, may inform future socio-technical design for automated vehicles and their human interactions.
Details
- Language :
- English
- ISSN :
- 26734052
- Volume :
- 3
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- Automation
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.332287662095469dba0e61dd80613a82
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/automation3030017