This paper examines how vehicles can quickly quantify the level of congestion in their environment for planning. We use risk level sets to define a metric of congestion for the vehicles. Using this metric, we can quickly identify distributions of environment and driver features, such as velocities and number of neighbors, based on risk within human driving data sets. We use the NGSIM and highD data sets to study how risk influences behaviors in city and highway driving. From these data sets, we learn common risk thresholds for classifying low, medium, and high-risk situations. Using these thresholds, we develop simulations of an autonomous vehicle driving along a highway, and demonstrate how the chosen risk threshold influences the autonomous vehicle behavior.