论文标题
边缘网络辅助实时对象检测框架用于自动驾驶
Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving
论文作者
论文摘要
自动驾驶汽车（AV）可以通过卸载任务甚至需要高计算功率（例如，对象检测（OD））到边缘云来实现所需的结果。但是，尽管利用边缘云，但由于动态的频道质量，无法总是保证实时OD。为了减轻此问题，我们提出了一个边缘网络辅助实时OD框架〜（EODF）。在EODF中，当通道质量不足以支持实时OD时，AVS提取了捕获图像的感兴趣区域〜（ROI）。然后，AVS基于ROI压缩图像数据，并将压缩的一个传输到边缘云。这样一来，由于传输延迟的减少，可以实现实时OD。为了验证我们的框架的可行性，我们评估了在框架间持续时间内未收到OD结果（即停机概率）及其准确性的概率。从评估中，我们证明了提出的EODF实时为AV提供了结果，并实现了令人满意的精度。
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.
