A human-like steering model

Sensitive to uncertainty in the environment

Conference paper (2017)

Authors

S.B. Kolekar Human-Robot Interaction - Mechanical, Maritime and Materials Engineering
J.C.F. de Winter Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
David Abbink Human-Robot Interaction - Mechanical, Maritime and Materials Engineering
Research Group
Human-Robot Interaction (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2017 S.B. Kolekar, J.C.F. de Winter, D.A. Abbink
DOI: https://doi.org/10.1109/SMC.2017.8122824
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Published Date

2017

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Human-Robot Interaction

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.

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