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：

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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，自动驾驶汽车（AV）有望通过分析各种车载传感器近乎实时地捕获的大量数据来感知周围环境。结果，将为AV引入巨大的计算成本，以使用已部署的机器学习（ML）模型来处理任务，而可能无法保证推理的准确性。在这种情况下，预计边缘智能（EI）和第六代（6G）无线网络的出现将为AV中的AV提供多路访问边缘计算（MEC）和ML，从而为更可靠，更安全的自动驾驶铺平道路。接近。为了实现这一目标，我们提出了两层由EI支持的自动驾驶框架。在自动驾驶汽车领域，通过拆分训练有素的深度神经网络模型，自动驾驶汽车被部署在浅层。在边缘智能层中，边缘服务器由其余层（也包括深层）和经过适当训练的多任务学习（MTL）模型实现。尤其是，获得最佳卸载策略（包括二进制卸载决策和计算资源分配）可以公式化为混合整数非线性规划（MINLP）问题，该问题可以通过MTL实时，高精度地解决。另一方面，提出了一种通过神经网络分割进行边缘车辆联合推理的方法，以实现有效的在线推理，同时保持数据隐私性，并减少通信延迟。实验证明了该框架的有效性，