Using artificial potential fields to model driver situational awareness

Recently, the use of artificial potential fields, known as risk fields, has been proposed for modeling human driver decision making. Such potential fields map from vehicle states and control inputs to a numerical risk measure such that the probability of choosing a control decreases as the risk associated increases. In this paper, we show that such a model can be used in a natural manner to also capture aspects of the driver's situational awareness, assuming that the risk fields govern their underlying behavior. We demonstrate our ideas on a specific obstacle avoidance scenario wherein obstacles to be avoided are placed in front of a driver at predicable intervals. Using data collected on a pilot experiment involving six different drivers using a high-fidelity driving simulator, we demonstrate the ability of our approach to capture the likelihood that the driver has perceived/reacted to the obstacle. Our approach works for scenarios when the driver collides with the obstacle as well as scenarios involving successful collision avoidance. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)