In this work, we explore the ability to estimate vehicle fuel consumption using imagery from overhead fisheye lens cameras deployed as traffic sensors. We utilize this information to simulate vision-based control of a traffic intersection, with a goal of improving fuel economy with
minimal impact to mobility. We introduce the ORNL Overhead Vehicle Data set (OOVD), consisting of a data set of paired, labeled vehicle images from a ground-based camera and an overhead fisheye lens traffic camera. The data set includes segmentation masks based on Gaussian mixture models for
vehicle detection. We show the data set utility through three applications: estimation of fuel consumption based on segmentation bounding boxes, vehicle discrimination for vehicles with large bounding boxes, and fine-grained classification on a limited number of vehicle makes and models using
a pre-trained set of convolutional neural network models. We compare these results with estimates based on a large open-source data set of web-scraped imagery. Finally, we show the utility of the approach using reinforcement learning in a traffic simulator using the open source Simulation
of Urban Mobility (SUMO) package. Our results demonstrate the feasibility of the approach for controlling traffic lights for better fuel efficiency based solely on visual vehicle estimates from commercial, fisheye lens cameras.