  Semantic segmentation is the problem of assigning a class label to every
pixel in an image, and is an important component of an autonomous vehicle
vision stack for facilitating scene understanding and object detection.
However, many of the top performing semantic segmentation models are extremely
complex and cumbersome, and as such are not suited to deployment onboard
autonomous vehicle platforms where computational resources are limited and
low-latency operation is a vital requirement. In this survey, we take a
thorough look at the works that aim to address this misalignment with more
compact and efficient models capable of deployment on low-memory embedded
systems while meeting the constraint of real-time inference. We discuss several
of the most prominent works in the field, placing them within a taxonomy based
on their major contributions, and finally we evaluate the inference speed of
the discussed models under consistent hardware and software setups that
represent a typical research environment with high-end GPU and a realistic
deployed scenario using low-memory embedded GPU hardware. Our experimental
results demonstrate that many works are capable of real-time performance on
resource-constrained hardware, while illustrating the consistent trade-off
between latency and accuracy.
