A human-like steering model: Sensitive to uncertainty in the environment

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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15 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2017)
EditorsAnup Basu, Witold Pedrycz, Xenophon Zabuli
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages1487-1492
ISBN (Electronic)978-1-5386-1645-1
DOIs
Publication statusPublished - 2017
EventSMC 2017: IEEE International Conference on Systems, Man, and Cybernetics - Banff, Canada
Duration: 5 Oct 20178 Oct 2017

Conference

ConferenceSMC 2017: IEEE International Conference on Systems, Man, and Cybernetics
CountryCanada
CityBanff
Period5/10/178/10/17

Keywords

  • Vehicles
  • Roads
  • Uncertainty
  • Adaptation models
  • Cost function
  • Predictive models
  • Trajectory

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