The emergence of mobile edge computing provides an efficient and stable computing platform for intelligent applications of autonomous vehicles, and deep neural network (DNN) based tasks collaborative inference through joint device-edge is considered an effective way to reduce latency. However, the computing resources allocated to the vehicle are dynamic as the number of requesters changes due to the limitation of edge server resources, which causes the best partition point of the DNN is not fixed. In this paper, we consider a dynamic resource allocation scheme to select the best partition point of DNN inference tasks by vehicle-edge collaborative computing. Specifically, the latency constrained DNN tasks of vehicles are partially offloaded to edge at the granularity of DNN layers. Considering the heterogeneity of vehicular computing capabilities and multiple DNN inference tasks, we formulate an optimization problem for dynamic resource allocation and automatically select the best partition point to minimize the overall latency of all vehicles, which is NP-hard. Then we design a chemical reaction optimization based algorithm for low complexity to solve the problem. The results of extensive evaluations illustrate that our proposed scheme is superior to other baseline schemes in terms of overall latency, and with lower failure rate.

移动边缘计算的出现为自动驾驶汽车的智能应用提供了一个高效，稳定的计算平台，通过基于联合设备边缘的基于深度神经网络（DNN）的任务协作推理被认为是减少延迟的有效方法。但是，由于边缘服务器资源的限制，随着请求者数量的变化，分配给车辆的计算资源是动态的，这导致DNN的最佳划分点不固定。在本文中，我们考虑一种动态资源分配方案，以通过车辆边缘协作计算来选择DNN推理任务的最佳划分点。具体而言，以DNN层的粒度将车辆的等待时间受约束的DNN任务部分卸载到边缘。考虑到车辆计算能力的异质性和多个DNN推理任务，我们制定了动态资源分配的优化问题，并自动选择最佳分区点以最小化所有车辆的总体等待时间，这是NP难的。然后，针对低复杂度，设计了一种基于化学反应优化的算法来解决该问题。大量评估的结果表明，我们提出的方案在总体延迟方面优于其他基线方案，并且故障率更低。然后，针对低复杂度，设计了一种基于化学反应优化的算法来解决该问题。大量评估的结果表明，我们提出的方案在总体延迟方面优于其他基线方案，并且故障率更低。然后设计了一种基于化学反应优化的低复杂度算法来解决该问题。大量评估的结果表明，我们提出的方案在总体延迟方面优于其他基线方案，并且故障率更低。