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

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 risk-sensitivity parameter and compares the prediction of the risk-neutral 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.