# Information Relaying Methods in VANET: Algorithms, Standards and Tests
## 2 Introduction  
V2X (Vehicle to Everything) technology has been widely studied and implemented for road trial purposes in many places all over the world. Due to its natural advantages of the real-time traffic situation sharing, the road situation early warning can be easily realized for the driving safety and efficiency, which is very much superior to the environment detection results generated only by onboard sensors. The VANET, as one of the well-known network architectures in the V2X use cases, has undergone significant investigations for its efficiency in routing messages to all other reachable vehicles and infrastructure within a given geographical area in a timely manner. One constraint of a VANET is the way that an appropriate relay vehicle is chosen and what the basic criteria is for determining the relay. Ref. [1] has studied a hybrid strategy for relay selection by considering the closest distance to the destination and best link status to bring in more robustness. Ref. [2] has considered the building shadow effect at intersections, utilizing the digital map and the Geohash coding system to relay the packets, and selecting the optimal relay node based on the relative position of the nodes and the intersection, to reduce the building shadow effect. Meanwhile, some other VANET relay papers focus on multi-hop relay, which is more practical in highway or suburban roadway scenarios. Ref. [3] has explored using the link quality as the key indicator for selecting multi-hop relay link paths to improve message reception performance over stringent communication conditions, and [4] has exploited the spatial diversity of the potential nodes from the sender for message reachability. For improving vehicle grouping of a multihop vehicular network, clustering is introduced, which is a well-known unsupervised classification of data samples in a given space via the selection procedure to minimize an objective function. The K-means-type algorithm, as one of the most popular types of clustering, is formed by evaluating the summation of dissimilarities between each data point and its corresponding cluster center [5; 6; 7; 8; 9]. The dissimilarity refers to the mean squared distance from each data point to its nearest center. Note that these cluster centers are virtual centroids used for attracting data points within  
Figure 1: IMT-2020 Wuhan large-scale interoperability test and Sony test devices. (**a**) Test road bird view map. (**b**) Sony test vehicles. (**c**) Sony test OBU. (**d**) Sony test RSU. their coverage and that no overlap is theoretically allowed between different data sections. In recent research, ref. [10] proposes an improved K-means algorithm by considering attributes with respect to vehicle movement, where the typical K-means tailored for actual VANET deployment adopts precise cluster head vehicle selection and threshold-based algorithm termination conditions. Ref. [11] also takes into account the initialization phase of cluster heads, which is to use a Maximum Stable Set Problem(MSSP) combined by the Continuous Hopfield Network (CHN) to secure a stable set of cluster head vehicles. Ref. [12] proposes the location based methods as well but with the assumption of the property of the destination vehicle as a cluster head. Some other relevant works considering cluster-based approaches on message routing principles are widely studied as well. In [13], in order to increase the route stability and average throughput, the current and destination location of vehicle nodes are used to select the optimal route via several routing protocols in different scenarios. Another cluster stability solution in [14] proposes a combination of a bully algorithm and a Lamport timestamp for selecting the better cluster head and for switching the cluster head in an efficient manner. Ref. [15] concentrates on direction of movement similarity for the selected cluster head and the neighbor one, to use their position coordinates and message forwarding destination, combing the direction of transmission of the message by the node.