  Autonomous vehicles (AVs) can achieve the desired results within a short
duration by offloading tasks even requiring high computational power (e.g.,
object detection (OD)) to edge clouds. However, although edge clouds are
exploited, real-time OD cannot always be guaranteed due to dynamic channel
quality. To mitigate this problem, we propose an edge network-assisted
real-time OD framework~(EODF). In an EODF, AVs extract the region of
interests~(RoIs) of the captured image when the channel quality is not
sufficiently good for supporting real-time OD. Then, AVs compress the image
data on the basis of the RoIs and transmit the compressed one to the edge
cloud. In so doing, real-time OD can be achieved owing to the reduced
transmission latency. To verify the feasibility of our framework, we evaluate
the probability that the results of OD are not received within the inter-frame
duration (i.e., outage probability) and their accuracy. From the evaluation, we
demonstrate that the proposed EODF provides the results to AVs in real-time and
achieves satisfactory accuracy.
