Multi-View Ensemble Learning With Missing Data: Computational Framework and Evaluations using Novel Data from the Safe Autonomous Driving Domain

Abstract

Real-world applications with multiple sensors observing an event are expected to make continuously-available predictions, even in cases where information may be intermittently missing. We explore methods in ensemble learning and sensor fusion to make use of redundancy and information shared between four camera views, applied to the task of hand activity classification for autonomous driving. In particular, we show that a late-fusion approach between parallel convolutional neural networks can outperform even the best-placed single camera model. To enable this approach, we propose a scheme for handling missing information, and then provide comparative analysis of this late-fusion approach to additional methods such as weighted majority voting and model combination schemes.