Sensor Training Data Reduction for Autonomous Vehicles
Matthew Tomei, Alexander Gerhard Schwing, Satish Narayanasamy, Rakesh Kumar · 2019
Ensuring safety and reliability of autonomous vehicles requires good learning models which, in turn, require a large amount of real-world training data. Data produced by in-vehicle sensors (e.g., cameras, LIDARs, IMUs, etc.) can be used for training; however, both local storage and transmission of this sensor data to the cloud for subsequent use in training can be prohibitively expensive due to the staggering volume of data produced by these sensors, especially the cameras. In this paper, we perform the first exploration of techniques for reducing video frames in a way that the quality of training for autonomous vehicles is minimally affected. We particularly focus on utility aware data reduction schemes where the potential contribution of a video frame to enhancing the quality of learning (or utility) is explicitly considered during data reduction. Since actual utility of a video frame cannot be computed online, we use surrogate utility metrics to decide what video frames to keep for training and which ones to discard. Our results show that utility-aware data reduction schemes can reduce the amount of camera data required for training by as much as $16\times$ compared to random sampling for the same quality of learning (in terms of IoU).