## 2 Related Works  
With the development of the economy, the level of car ownership is rising, the number of vehicle users is increasing, and the rate requirements for CAEVs IoT are also increasing. To meet the demand of various application services of CAEVs IoT and to utilize the available communication resources more effectively, more and more researchers focus on resource allocation optimization in the field of CAEVs IoT. In the literature [10], the authors build an SDN-assisted MEC network architecture for in-vehicle networks and improve the efficiency and flexibility of in-vehicle networks by introducing SDN controllers. The optimal offloading decision, transmission power control, sub-channel allocation and computational resource allocation schemes are also given for the V2X offloading and resource allocation problems. The literature [11] proposed a DNN model partitioning strategy, then proposed a content-driven joint resource allocation scheme based on vehicle-side collaboration, and designed a multi-intelligent distributed Q-learning algorithm to solve the multi-constrained non-linear planning arising from the vehicle network resource allocation A multi-intelligence distributed Q-learning algorithm is designed to solve the multi-constrained non-linear planning problem arising from the resource allocation of vehicular networks. The literature [12] reported increasing the system capacity by introducing NOMA and improved the network performance by centralized resource management that allows D2D resource sharing based on spatial reuse among all V2X communication groups. The literature [13] decomposed the problem of optimizing the average delay of downloading files from a system vehicle into a communication resource optimization allocation subproblem and a cache resource optimization allocation and file placement selection subproblem, and proposed a joint resource allocation algorithm based on network slicing. In the literature [14], in order to satisfy the maximum delay and residence time constraints of vehicles, a fog-based vehicle network was used to balance the number of shared messages and constrained resources, and decouple the original problem into two independent subproblems and design solutions to solve them. To address the highly dynamic nature of the V2X environment, the literature [15] introduced a mapping fuzzy space to effectively avoid the deterioration of this dynamic environment on the stability and convergence of the optimization mechanism, and constructed a bilateral many-to-many fuzzy matching game (MM-FMG) to formulate the optimization problem with dynamic and uncertain information on this basis, finally proposing a vehicle resource dynamic matching algorithm to solve the problem. Most of the current resource allocation algorithms for CAEVs IoT are focused on the allocation of on-board information resources, and the scope of research is relatively limited. However, with the continuous development of communication technology, new technologies such as millimetre wave communication, radar and satellite communication are emerging, providing a wide creative space for the development of CAEVs research scholars and unlimited possibilities for the development of telematics technology. The existing resource allocation method based on the ACA has the disadvantages of a long span, high cost and easy to produce local optimal solutions, making its application in resource-scheduling problems much less valuable. Therefore, most scholars optimize the ACA to improve the performance of the algorithm in three aspects: time, cost, and load by adding constraints. In addition to considering time, cost, and load, the improvement of the ACA should also consider the security, reliability, and delay of resource allocation for CAEVs IoT. However, existing studies rarely consider these factors together and propose effective optimization improvement methods. Our proposed method tries to introduce the RED algorithm and ACA to complement each other, thus improving the drawbacks of the ACA.