# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 6 Discussion of Future Research Directions  
The rapidly growing paradigm trend of Internet of Vehicles predicts that it will gain attention and development in many fields and industries in the next decade. The main goal of applying collaborative intelligence ideas and technologies to the field of IoV is to integrate the data and resources of a large number of vehicles, users, infrastructure, and networks, providing systems with reliability and connectivity that are easy to manage, control, and operate. Due to the dynamic nature of vehicles, the diversity of devices, the low time delay requirements of applications, and the connectivity of V2X, various special requirements are put forward for the collaboration of this complex system. However, these aspects bring new technical challenges to the design and expansion of the IoV field. Important future research directions that will help to conquer those challenges are presented in Figure 7, as follows:  
* Big Data Management: In IoV under collaborative intelligence, multiple types of smart devices generate a large amount of data, which are stored locally or in the cloud. Network latency and insufficient storage affect computation and analysis, and may even break the system. Therefore, the real-time management and analysis of IoV big data has always been a challenge, but will also be a challenge in the future. (1) Comprehensive awareness of the environment: Fusing sensor data and historical knowledge in the collaborative network provides comprehensive awareness of the environment for the whole system and each entity, so as to improve the accuracy and security of decision making. (2) Storage and preprocessing of big data: Effectively utilizing the storage capacity of edge nodes and cloud data centers to further compress the increasing massive data and eliminate a large amount of redundancy is another important research direction. (3) Data transmission and unified standards: In the case of vehicle movement and low latency, big data have more stringent requirements on communication, so reducing the amount of transmitted data and improving communication efficiency is inevitable. Research on more reasonable communication protocols, design of edge computing models with a small amount of data transmission, and use of semantic communication to compress data are all directions which researchers can make further efforts to explore. Furthermore, establishing a unified transmission standard and increasing investment in infrastructure can greatly accelerate the development of IoV. * Sparse Data: Since most collaborative intelligence requires machine learning methods that are data-driven or interact with the environment, a large amount of training data will be used to train a model for a certain task. This puts forward high requirements for data collection and processing, but some tasks make it difficult to collect corresponding high-quality data in reality, resulting in the problem of data sparsity. In order to overcome this problem, many studies use realistic simulators to generate simulated data for model training, and then transfer to real data to fine-tune the model. On the other hand, the learning model is built and adjusted, or other intelligent methods are combined to optimize the training effect. In addition, expanding the scope of collaboration and deepening the cooperation in collaboration can alleviate the problem of data sparsity to a certain extent. This is also an aspect to which future researchers can devote their energy. * Stability: Vehicles are dynamic in nature, and the network topology of IoV also changes at any time, so stability is also a major challenge in this field. The stability is reflected in the network connectivity, and the data and signal transmission between agents need high-quality communication capabilities.