## 3 Required Data Rate in V2V for Safe Overtaking  
### Cooperative Perception and Scenario Description  
Once a route destination is decided, automated vehicles calculate a suitable route according to context information, e.g., traffic congestion and toll fees. Information-enriched maps, called dynamic maps, are considered as a promising tool to provide such information to automated vehicles. Dynamic maps consist of static information and dynamic information. In detail, the dynamic information includes information about surrounding moving obstacles such as vehicles and pedestrians, and static information includes HD 3D geographic information, e.g., 3D object distribution and lane information. Automated vehicles need such dynamic information in order to detect and avoid obstacles. Since dynamic maps must provide various information to perform detection and avoidance, automated vehicles are equipped with various sensors such as radar sensors, stereo cameras, and LiDAR sensors. These sensors provide specific data and information about the vehicle environment and update the dynamic map continuously. LiDAR/radar sensors measure the time it takes for an optical/electrical pulse to return to the LiDAR/radar sensor and provide the distance to the object. Cameras can capture objects' shapes and movements and even estimate distance by parallax angles with stereoscopic setups. The detailed characteristics of the three sensors are the following:  
* LiDAR sensors provide the distance to an object, and the accuracy is significantly higher than from a radar sensor. Therefore, it can generate a precise 3D map of the surroundings, but it is hard to provide high accuracy data in bad weather, and LiDAR sensors generate a large amount of data. * Radar sensors estimate the velocity, distance, and angle of an object and can work in bad weather, but have difficulty providing high-accuracy data. * Cameras are good at the classification of objects because they can see color, but the operation is degraded in bad weather or when there is dust in the optical path. In this paper, we focused on LiDAR sensors in more detail, because these sensors contribute to creating HD dynamic maps, and their output data rate is dominant among automotive sensors, which has a great effect on cooperative perception. According to the measurement principle of LiDAR sensors, when an obstacle is in the FOV (Field-Of-View) of a LiDAR sensor, it can provide the location of the obstacle. On the other hand, it cannot provide any information about an obstacle in a blind spot. Furthermore, as an obstacle that partially blocks a LiDAR sensor's FOV approaches the LiDAR sensor, the visible area of the LiDAR sensor becomes narrower. As a result, the ability to recognize all obstacles by using only onboard sensors becomes extremely low. To obtain information about blind spots, sharing other sensor data through V2V and V2I (Vehicle-to-Infrastructure) communication is proposed. Sharing sensor data between vehicles is often called cooperative perception. The main advantage of cooperative perception is that it can provide an extended sensing area without substantial additional costs. This additional sensing area contributes to improving traffic efficiency, as well as traffic safety. In [17], it was shown that cooperative perception effectively helped to trigger the early lane changing in the experiment, which contributed to comfortable and safe driving. The resolution of onboard sensors is an important factor in cooperative perception or recognition. Furthermore, the performance of communication is also an important factor in cooperative perception. Since automated vehicles are equipped with many sensors to recognize their surrounding obstacles, the amount of the generated data is very large, and these data need to be transmitted and processed within the given latency requirements. So far, there are two types of standardized V2V communication systems. One is IEEE (Institute of Electrical and Electronics Engineers) 802.11p, which is one of the dedicated short-range communications.