The interaction between a human driver and an automated driving system may improve when the automation is designed in such a way that it behaves in a human-like manner. This paper introduces a human-like steering model, in which the driver adapts to the risk due to uncertainty in the environment. Current steering models take a risk-neutral approach, while the fields of economics and sensorimotor control suggest that humans exhibit risk-sensitive behavior. The proposed model uses a risk-sensitive optimal feedback control structure to predict steering behavior. The paper studies the effect of the risksensitivity parameter and compares the prediction of the riskneutral and risk-sensitive controllers in a simulated abstraction of two scenarios: (a) driving while being subjected to lateral wind gusts and (b) overtaking an unpredictably swerving car. The simulation results show that the risk-sensitive model adapts to the uncertainty in the environment. Experimental data will be needed to validate the predictions of our model.
|Title of host publication||Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2017)|
|Editors||Anup Basu, Witold Pedrycz, Xenophon Zabuli|
|Place of Publication||Piscataway, NJ, USA|
|Publication status||Published - 2017|
|Event||SMC 2017: IEEE International Conference on Systems, Man, and Cybernetics - Banff, Canada|
Duration: 5 Oct 2017 → 8 Oct 2017
|Conference||SMC 2017: IEEE International Conference on Systems, Man, and Cybernetics|
|Period||5/10/17 → 8/10/17|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- Adaptation models
- Cost function
- Predictive models