Camara, F, Romano, R orcid.org/0000-0002-2132-4077, Markkula, G orcid.org/0000-0003-0244-1582 et al. (3 more authors) (2018) Empirical game theory of pedestrian interaction for autonomous vehicles. In: Grant, R, Allen, T, Spink, A and Sullivan, M, (eds.) Proceedings of Measuring Behavior 2018. Measuring Behavior 2018: 11th International Conference on Methods and Techniques in Behavioral Research, 06-08 Jun 2018, Manchester, UK. Manchester Metropolitan University , pp. 238-244. ISBN 978-1-910029-39-8
Abstract
Autonomous vehicles (AV’s) are appearing on roads, based on standard robotic mapping and navigation algorithms. However their ability to interact with other road-users is much less well understood. If AVs are programmed to stop every time another road user obstructs them, then other road users simply learn that they can take priority at every interaction, and the AV will make little or no progress. This issue is especially important in the case of a pedestrian crossing the road in front of the AV. The present methods paper expands the sequential chicken model introduced in (Fox et al., 2018), using empirical data to measure behavior of humans in a controlled plus-maze experiment, and showing how such data can be used to infer parameters of the model via a Gaussian Process. This providing a more realistic, empirical understanding of the human factors intelligence required by future autonomous vehicles.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | This is an author produced version of a paper published in Proceedings of Measuring Behavior 2018: 11th International Conference on Methods and Techniques in Behavioral Research. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Funding Information: | Funder Grant number EU - European Union 723395 |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Apr 2018 15:24 |
Last Modified: | 09 Jul 2019 15:25 |
Status: | Published |
Publisher: | Manchester Metropolitan University |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129303 |