In a level-5 autonomous driving system, the autonomous driving vehicles (AVs) are expected to sense the surroundings via analyzing a large amount of data captured by a variety of onboard sensors in near-real-time. As a result, enormous computing costs will be introduced to the AVs for processing the tasks with the deployed machine learning (ML) model, while the inference accuracy may not be guaranteed. In this context, the advent of edge intelligence (EI) and sixth-generation (6G) wireless networking are expected to pave the way to more reliable and safer autonomous driving by providing multi-access edge computing (MEC) together with ML to AVs in close proximity. To realize this goal, we propose a two-tier EI-empowered autonomous driving framework. In the autonomous-vehicles tier, the autonomous vehicles are deployed with the shallow layers by splitting the trained deep neural network model. In the edge-intelligence tier, an edge server is implemented with the remaining layers (also deep layers) and an appropriately trained multi-task learning (MTL) model. In particular, obtaining the optimal offloading strategy (including the binary offloading decision and the computational resources allocation) can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which is solved via MTL in near-real-time with high accuracy. On another note, an edge-vehicle joint inference is proposed through neural network segmentation to achieve efficient online inference with data privacy-preserving and less communication delay. Experiments demonstrate the effectiveness of the proposed framework, and open research topics are finally listed.

中文翻译：

---

6G 无线系统中自动驾驶的边缘智能：设计挑战和解决方案


在 5 级自动驾驶系统中，自动驾驶车辆（AV）预计通过分析各种车载传感器捕获的大量数据来近实时地感知周围环境。因此，自动驾驶汽车使用部署的机器学习（ML）模型处理任务时会产生巨大的计算成本，而推理精度可能无法保证。在此背景下，边缘智能（EI）和第六代（6G）无线网络的出现预计将为自动驾驶提供多路访问边缘计算（MEC）和机器学习，从而为更可靠、更安全的自动驾驶铺平道路。距离很近。为了实现这一目标，我们提出了一个两层 EI 驱动的自动驾驶框架。在自动驾驶车辆层中，通过分割经过训练的深度神经网络模型，将自动驾驶车辆部署到浅层。在边缘智能层中，边缘服务器是通过其余层（也是深层）和经过适当训练的多任务学习（MTL）模型来实现的。特别是，获得最佳卸载策略（包括二进制卸载决策和计算资源分配）可以表示为混合整数非线性规划（MINLP）问题，通过 MTL 近乎实时地高精度求解。另一方面，通过神经网络分割提出了边缘车辆联合推理，以实现高效的在线推理，同时保护数据隐私和减少通信延迟。实验证明了所提出框架的有效性，并最终列出了开放的研究课题。