Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection
  in Autonomous Driving
Faisal Hawlader François Robinet Raphaël Frank
Environmental perception is a key element of autonomous driving because the
information received from the perception module influences core driving
decisions. An outstanding challenge in real-time perception for autonomous
driving lies in finding the best trade-off between detection quality and
latency. Major constraints on both computation and power have to be taken into
account for real-time perception in autonomous vehicles. Larger object
detection models tend to produce the best results, but are also slower at
runtime. Since the most accurate detectors cannot run in real-time locally, we
investigate the possibility of offloading computation to edge and cloud
platforms, which are less resource-constrained. We create a synthetic dataset
to train object detection models and evaluate different offloading strategies.
Using real hardware and network simulations, we compare different trade-offs
between prediction quality and end-to-end delay. Since sending raw frames over
the network implies additional transmission delays, we also explore the use of
JPEG and H.265 compression at varying qualities and measure their impact on
prediction metrics. We show that models with adequate compression can be run in
real-time on the cloud while outperforming local detection performance.