LAUSR

A key challenge for self-driving vehicle researchers is to curate massive instrumented vehicle datasets. A common task in their development workflow is to extract video segments that meet particular criteria, such as a particular road scenario or vehicle maneuver. We present a novel approach for detecting vehicle maneuvers from monocular dashboard camera video building upon a deep learning visual odometry model (DeepV2D) to estimate frame-accurate ego-vehicle movement. We leverage image classification and lane line estimation to extend our technique. We classify movement sequences against reference maneuvers using dynamic time warping. We describe, implement, and evaluate classifiers to recognize maneuvers such as turns, lane changes, and deceleration. We show that using deep learning visual odometry to estimate location is superior to consumer-grade high-resolution GPS for this application. We describe and implement a greedy approach to classify maneuvers and evaluate our approach on common road maneuvers. We find an overall AUROC value of 0.91 for turns and 0.84 for all maneuvers in our dataset.