Next-Generation Edge Computing Assisted Autonomous Driving Based Artificial Intelligence Algorithms

Abstract

Edge Computing and Network Function Virtualization (NFV) concepts can improve network processing and multi-resources allocation when intelligent optimization algorithms are deployed. Multiservice offloading and allocation approaches pose interesting challenges in the current and nextgeneration vehicle networks. The state-of-the-art optimization approaches still formulate exact algorithms, and tune approximation methods to get sufficient solutions. These approaches are data-centric that aim to use heterogeneous data inputs to find the near optimal solutions. In the context of connected and autonomous vehicles (CAVs), these techniques show an exponential computational time and deal only with small and medium scale networks. Therefore, we are motivated by using recent Deep Reinforcement Learning (DRL) techniques to learn the behavior of exact optimization algorithms while enhancing the Quality of Service (QoS) of network operators and satisfying the requirements of the next-generation Autonomous Vehicles (AVs). DRL algorithms can improve AVs service offloading and optimize edge resources. An Optimal Virtual Edge Autopilot Placement (OVEAP) algorithm is proposed using Integer Linear Programming (ILP). Moreover, an autopilot placement protocol is presented to support the algorithm. Optimal allocation and Virtual Network Function (VNF) placement and chaining of the autopilot, based on several new constraints such as computing and networking loads, network edge infrastructure, and placement cost, are designed. Further, a DRL approach is formulated to deal with dense Internet of Autonomous Vehicle (IoAV) networks. Extensive simulations and evaluations are carried out. Results show that the proposed allocation strategies outperform the state-of-the-art solutions and give better performance in terms of Total Edge Servers Utilization, Total Edge Servers Allocation Time, and Successfully Allocated autopilots.

6G‐edge support of Internet of Autonomous Vehicles: A survey

“…4. Vehicle to edge communication protocol 12 : Several protocols were considered such as ZeroMQ, RTMP/RTSP sockets, ROS2. This latter should be considered as it provides real time data transfer across the network.…”

“…All autopilot functionalities are computed in the edge. It encompasses all the embedded autopilot features working on the edge 12,13 . The main stakeholders involved in this phase are OEMs, network operator, edge service function providers and end users. Hybrid autopilot: In this phase, the CAV has a back‐up brain for the embedded autopilot (phase 1).…”

“…This decision is sent back to the vehicle like speed, brake and so forth. This decision can be verified by embedded autopilot, and then it can take the decision based on augmented information 12,14 . The main stakeholders involved in this phase are OEMs, network operator, embedded/edge service function providers and end users.…”

“…It encompasses all the embedded autopilot features working on the edge. 12,13 The main stakeholders involved in this phase are OEMs, network operator, edge service function providers and end users. 3.…”

Test on High Pressure Leakage of Diesel Needle Valve Coupler Based on Artificial Intelligence Algorithm

“…Through research, it is found that there are many theoretical achievements based on artificial intelligence and the theory of high-pressure leakage of diesel needle valve coupling parts. For example, some scholars put forward that with the continuous tightening of diesel engine emission regulations, the injection pressure of diesel engine is getting higher and higher, and the fuel leakage of the fitting clearance of high-pressure injection pressure bearing coupling will have an impact on the injection process [1][2]. Some scholars also pointed out that in order to further improve the working condition of fuel injection atomization, the test of increasing the opening pressure of the injection needle valve pair should be carried out [3][4].…”