  Neural networks have become the standard model for various computer vision
tasks in automated driving including semantic segmentation, moving object
detection, depth estimation, visual odometry, etc. The main flavors of neural
networks which are used commonly are convolutional (CNN) and recurrent (RNN).
In spite of rapid progress in embedded processors, power consumption and cost
is still a bottleneck. Spiking Neural Networks (SNNs) are gradually progressing
to achieve low-power event-driven hardware architecture which has a potential
for high efficiency. In this paper, we explore the role of deep spiking neural
networks (SNN) for automated driving applications. We provide an overview of
progress on SNN and argue how it can be a good fit for automated driving
applications.
