  Detection of surrounding objects and their motion prediction are critical
components of a self-driving system. Recently proposed models that jointly
address these tasks rely on a number of sensors to achieve state-of-the-art
performance. However, this increases system complexity and may result in a
brittle model that overfits to any single sensor modality while ignoring
others, leading to reduced generalization. We focus on this important problem
and analyze the contribution of sensor modalities towards the model
performance. In addition, we investigate the use of sensor dropout to mitigate
the above-mentioned issues, leading to a more robust, better-performing model
on real-world driving data.
