  While transformer architectures have dominated computer vision in recent
years, these models cannot easily be deployed on hardware with limited
resources for autonomous driving tasks that require real-time-performance.
Their computational complexity and memory requirements limits their use,
especially for applications with high-resolution inputs. In our work, we
redesign the powerful state-of-the-art Vision Transformer PLG-ViT to a much
more compact and efficient architecture that is suitable for such tasks. We
identify computationally expensive blocks in the original PLG-ViT architecture
and propose several redesigns aimed at reducing the number of parameters and
floating-point operations. As a result of our redesign, we are able to reduce
PLG-ViT in size by a factor of 5, with a moderate drop in performance. We
propose two variants, optimized for the best trade-off between parameter count
to runtime as well as parameter count to accuracy. With only 5 million
parameters, we achieve 79.5$\%$ top-1 accuracy on the ImageNet-1K
classification benchmark. Our networks demonstrate great performance on general
vision benchmarks like COCO instance segmentation. In addition, we conduct a
series of experiments, demonstrating the potential of our approach in solving
various tasks specifically tailored to the challenges of autonomous driving and
transportation.
