  One of the major open challenges in self-driving cars is the ability to
detect cars and pedestrians to safely navigate in the world. Deep
learning-based object detector approaches have enabled great advances in using
camera imagery to detect and classify objects. But for a safety critical
application, such as autonomous driving, the error rates of the current state
of the art are still too high to enable safe operation. Moreover, the
characterization of object detector performance is primarily limited to testing
on prerecorded datasets. Errors that occur on novel data go undetected without
additional human labels. In this letter, we propose an automated method to
identify mistakes made by object detectors without ground truth labels. We show
that inconsistencies in the object detector output between a pair of similar
images can be used as hypotheses for false negatives (e.g., missed detections)
and using a novel set of features for each hypothesis, an off-the-shelf binary
classifier can be used to find valid errors. In particular, we study two
distinct cues - temporal and stereo inconsistencies - using data that are
readily available on most autonomous vehicles. Our method can be used with any
camera-based object detector and we illustrate the technique on several sets of
real world data. We show that a state-of-the-art detector, tracker, and our
classifier trained only on synthetic data can identify valid errors on KITTI
tracking dataset with an average precision of 0.94. We also release a new
tracking dataset with 104 sequences totaling 80,655 labeled pairs of stereo
images along with ground truth disparity from a game engine to facilitate
further research. The dataset and code are available at
https://fcav.engin.umich.edu/research/failing-to-learn

Computer Vision and Pattern Recognition;Robotics