## 5 Discussion and Future Works
### CAV Integrated with the Cloud  
Recently, much work has considered integrating the cloud with CAV systems [129; 130]. On one hand, the cloud provides resourceful information to CAVs; on the other hand, the cloud can provide powerful resources to support large storage and fast computation,which is indispensable for implementing some sophisticated AI algorithms. Therefore, the cloud shows great advantages to secure CAVs. In essence, a CAV integrated with the cloud is a large-scale hybrid platform composed of a remote centralized infrastructure cloud and distributed nodes with fog/edge computing. The architecture of the platform is shown in Figure 7. Locally, each vehicle and RSU/BS are enhanced with extra computing units and storage to process some data/information to meet the requirement of time-sensitive applications. Meanwhile, multiple types of commercial clouds, such as Microsoft Azure and AWS, are readily available to cooperate with the possible automaker clouds and traffic center clouds in the future, to support some advanced functions with heavily computing overload. Typically, data collected from local sensors have a direct impact on the vehicle's safety by providing obstacle detection and navigation operation. With the assistance of the cloud, these data can be processed and explored remotely to provide complementary measures to enhance vehicle security and safe operations. In the future, enormous volumes of diverse types of messages will be transmitted through vehicular communication channels. Due to the limited capacities of the communication channels, all messages will compete for the temporal and spatial resources, which can be easily utilized by malicious attackers to launch resource-availability attacks. Meanwhile, it is inevitable that many messages are collected by vehicles from the same region, which indicates that great duplication and redundancy exists in the messages. With cloud involvement, these messages can be merged and pruned through data relevance analysis before they are re-transmitted to a region or a certain number of vehicles. By transmitting compact but useful messages the communication channels utility can be improved. Cyber-attacks, such as DoS/spoofing, may be prevented. Furthermore, the cloud can collect information from multiple vehicles in a region to generate reports about traffic flow, traffic density, and traffic speed of that region. This way, the cloud can provide vehicles with reference and surveillance for safe driving. For example, being aware of the average traffic speed provided by cloud reports, autonomous vehicles can recognize the obviously abnormal behaviors of some vehicles in time to avoid malicious  
Figure 7: Architecture of CAVs integrated with the cloud. operations or potential dangers. Another good example is that, in bad weather (e.g., heavy rain or storm), the perception of local sensors is easily susceptible, even completely disabled. In this case, the transmitted messages can provide navigation and guidance to the vehicle for safe movement. However, there are some issues which should be well considered when the cloud is deployed in practice. Typically, the preliminary aggregation and fusion of the raw data can be achieved within local fog/vehicular nodes, which is applicable to the low-delay applications, such as emergency navigation and obstacle recognition. Considering high-level fusion, either feature fusion or decision fusion, it typically has a loss of information and needs co-relation through complicated algorithms (e.g., support vector machine or kernel-based clustering), which are time/resource-consuming. Therefore, it can be achieved on the remote cloud to meet the requirements of delay-tolerant applications. It is still an open issue how to assign the assignments to local fog/edge computing and remote cloud computing. On the other hand, as we mentioned in cyber-attacks on intra-vehicle systems, malware would be a type of smart attack, which will be greatly threatening to CAVs in the future.