The field of image and video quality assessment has enjoyed rapid development over the last two decades. Several datasets and algorithms have been designed to understand the effects of common distortions on the subjective experiences of human observers. The distortions present in these datasets may be synthetic (applying artificially computed blur, compression, noise, etc.) or authentic (in-capture lens flare, motion blur, under/overexposure, etc.). The goal of quality assessment is often to quantify the loss of visual "naturalness" caused by the distortion(s). We have recently created a new resource called LIVE-RoadImpairs, which is a novel image quality dataset consisting of authentically distorted images of roadways. We use the dataset to develop a no-reference quality assessment algorithm that is able to predict the failure rates of object-detection algorithms. This work was among the overall winners of the PSCR Enhancing Computer Vision for Safety Challenge.