论文标题
从自动驾驶汽车测试事故中学到的经验教训：边缘学习辅助卸载框架
Lessons Learned from Accident of Autonomous Vehicle Testing: An Edge Learning-aided Offloading Framework
论文作者
论文摘要
这封信提出了一个基于边缘学习的卸载框架，用于自动驾驶，其中可以将深度学习任务卸载到边缘服务器，以提高推理精度，同时满足延迟约束。由于无线通信和计算产生了延迟和推理精度，因此提出了优化问题，以最大程度地提高推理准确性，但根据卸载概率，前制动前概率和数据质量。模拟证明了提议的卸载框架的优越性。
This letter proposes an edge learning-based offloading framework for autonomous driving, where the deep learning tasks can be offloaded to the edge server to improve the inference accuracy while meeting the latency constraint. Since the delay and the inference accuracy are incurred by wireless communications and computing, an optimization problem is formulated to maximize the inference accuracy subject to the offloading probability, the pre-braking probability, and data quality. Simulations demonstrate the superiority of the proposed offloading framework.
