In this paper, we use the concept of artificial risk fields to predict how
human operators control a vehicle in response to upcoming road situations. A
risk field assigns a non-negative risk measure to the state of the system in
order to model how close that state is to violating a safety property, such as
hitting an obstacle or exiting the road. Using risk fields, we construct a
stochastic model of the operator that maps from states to likely actions. We
demonstrate our approach on a driving task wherein human subjects are asked to
drive a car inside a realistic driving simulator while avoiding obstacles
placed on the road. We show that the most likely risk field given the driving
data is obtained by solving a convex optimization problem. Next, we apply the
inferred risk fields to generate distinct driving behaviors while comparing
predicted trajectories against ground truth measurements. We observe that the
risk fields are excellent at predicting future trajectory distributions with
high prediction accuracy for up to twenty seconds prediction horizons. At the
same time, we observe some challenges such as the inability to account for how
drivers choose to accelerate/decelerate based on the road conditions.Comment: 8 pages, 4 figures, submitted to Intelligent Transportation Systems
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