  Current Deep Learning methods for environment segmentation and velocity
estimation rely on Convolutional Recurrent Neural Networks to exploit
spatio-temporal relationships within obtained sensor data. These approaches
derive scene dynamics implicitly by correlating novel input and memorized data
utilizing ConvNets. We show how ConvNets suffer from architectural restrictions
for this task. Based on these findings, we then provide solutions to various
issues on exploiting spatio-temporal correlations in a sequence of sensor
recordings by presenting a novel Recurrent Neural Network unit utilizing
Transformer mechanisms. Within this unit, object encodings are tracked across
consecutive frames by correlating key-query pairs derived from sensor inputs
and memory states, respectively. We then use resulting tracking patterns to
obtain scene dynamics and regress velocities. In a last step, the memory state
of the Recurrent Neural Network is projected based on extracted velocity
estimates to resolve aforementioned spatio-temporal misalignment.

Computer Vision and Pattern Recognition;Machine Learning