Experimental Evaluation of a Game-Theoretic Human Driver Steering Control Model.
View / Open Files
Publication Date
2022-01-25Journal Title
IEEE Trans Cybern
ISSN
2168-2267
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Na, X., & Cole, D. J. (2022). Experimental Evaluation of a Game-Theoretic Human Driver Steering Control Model.. IEEE Trans Cybern https://doi.org/10.1109/TCYB.2022.3140362
Abstract
Automated vehicle steering control systems have great potential to improve road safety. The development of such systems calls for mathematical driver models able to represent human drivers' steering behavior in response to automated steering intervention. This article concerns the experimental evaluation of a game-theoretic driver steering control model. The driver model centers on a steering control strategy developed based on the Nash equilibrium of a theoretic noncooperative game between the driver and automated steering controller. The key parameters of the game-theoretic driver model are identified by fitting the model to real driver steering behavior measured from six driver subjects in an experiment using a driving simulator. The game-theoretic driver model is evaluated by compared to a ``conventional'' optimal-control-theoretic driver model, and analyzing their model fitting errors. Results from the analysis demonstrate that the game-theoretic driver model is statistically significantly better than the conventional driver model for representing three out of the six subjects' steering behavior. For the other three subjects, both the two models perform statistically equivalently well.
Identifiers
External DOI: https://doi.org/10.1109/TCYB.2022.3140362
This record's URL: https://www.repository.cam.ac.uk/handle/1810/332867
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.
Recommended or similar items
The current recommendation prototype on the Apollo Repository will be turned off on 03 February 2023. Although the pilot has been fruitful for both parties, the service provider IKVA is focusing on horizon scanning products and so the recommender service can no longer be supported. We recognise the importance of recommender services in supporting research discovery and are evaluating offerings from other service providers. If you would like to offer feedback on this decision please contact us on: support@repository.cam.ac.uk