# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 6 Discussion of Future Research Directions
Although there are many research studies on the cooperative communication of Internet of Vehicles, the Internet facilities for Internet of Vehicles or Internet of Things are not perfect in most areas, and it will take some time to achieve coverage of 5G and the development of 6G. Therefore, the real realization of collaborative intelligent communication in the Internet of Vehicles still needs efforts. * Reliability: Applications related to intelligent transportation and UAV detection are usually sensitive to safety, because such applications require high reliability, otherwise there will be serious losses of life and property. In the application of Internet of Vehicles, reliability is an important issue due to the large scale of the network, complex computing architecture, and poor network stability. * Different Purposes, Different models: Collaborative intelligent models based on machine learning are widely used in Internet of Vehicles tasks and have achieved good results, such as reinforcement learning, federated learning, centralized learning, etc. However, in different tasks and objectives, different learning models and methods are usually used. The data demand is large, the processing is tedious, the model parameters are particularly large, the training time is long, and the model is not universal. In future research, this problem can be optimized from two perspectives. (1) Building a unified large model that integrates data and is suitable for multi-class tasks after a simple fine-tuning process. (2) Constructing different models for different purposes to refine tasks and achieve better results. * Safety: Safety is one of the core challenges in the field of Internet of Vehicles. It is a network that is connected by a variety of devices and integrates different technologies and standards through the Internet to achieve collaborative intelligence. Not only must infrastructure such as roadside units, cloud storage centers, and computing centers be connected to the network, but also most of the vehicle's devices such as GPS, cameras, sensors, brakes, and accelerators may be accessed remotely. So if the network security is not in place, the attacker may control a large number of network devices or even directly control the vehicle, leading to serious consequences. At present, IoV is not completely secure against all kinds of attacks, such as quantum attacks. Therefore, the discovery and repair of security vulnerabilities, and even the establishment of a more perfect security system are indispensable challenges in the future. * Privacy Protection: In V2X network, the everything-connected network will create the problem of data and privacy exposure. How to protect user privacy and data is also a very recent concern, including the application of federated learning and blockchain technology in IoV collaboration. The most popular solution in this regard is to use federated learning and blockchain technology, which can avoid the direct transmission of original user data to make task decisions and take advantage of the decentralization and tamper-proof characteristics of blockchain to improve the privacy protection ability. However, the cost of applying these technologies in IoV collaboration is not low, and there are unsolved difficulties. * Convergence: The problem of training efficiency of machine learning models is often involved in coordinated intelligence methods. Under the strict service requirements of high security, stability and low latency, methods that help the model achieve higher learning accuracy and faster convergence speed are worthy of further improvement. * Combination with other approaches: At present, the method of deep learning is widely used to realize the collaborative intelligence of Internet of Vehicles, and has achieved good results. On this basis, other effective ideas and approaches, such as fuzzy logic, semantic communication, and digital twins, can be combined to further optimize the task results.