## 2 Proposed Road Surveillance System  
This paper proposes a winter road surveillance system consisting of one of the QEF sensors on the vehicles, an AAA system with machine learning-based image recognition, and V2V-based DTN routing as shown in Figure 1.  
Figure 1: Proposed winter road surveillance system.  
In the system, first of all, the road conditions are estimated by the QEF and temperature sensors on vehicles, and the estimated data are stored in each vehicle. Then, when there are vehicles which can receive transmissions or wireless devices on the utility poles along the roads, the data are communicated to others via DTN routing using the data triage methods [10; 11].  
Therefore, the current road surveillance systems usually consists of sensors such as cameras or thermometers at traffic lights or utility poles along roads, and the observed points and time durations are strictly limited. On the other hand, the proposed road surveillance system can provide more observed points and the recent data of the mountain roads.  
However, as mentioned in the previous section, it is necessary to consider some issues to realize V2V communication in the proposed system. One of the problems is the radio range of V2V communication. Since vehicles tend to move fast in local areas, the transmittable distances are not enough to establish connections and to manipulate the data using high-frequency radio bands with a nondirectional antenna, such as IEEE802.11p. Therefore, the data duplications by IEEE802.11p must be completed within several seconds while the vehicles are running. Further, the LPWA (low power, wide area) [12] has a longer transmission range because it consists of a lower frequency, such as 920 MHz, but the throughput is not enough for the transmission of multimedia data such as movies or pictures.  
Secondly, it is necessary to consider the radio noises from obstacles such as trees or houses in the proposed V2V communication. As shown in Figure 1, if there is a tree between the running vehicles, the high-frequency radio can easily cause radio noises, and it is hard to maintain a stable wireless connection in the surveillance system. Further, the optimal antenna direction is hard to set because of the driving vehicles. Even if the optimal antenna directions of the AAA system are set from the current vehicle's position, vehicles with high speeds will move to the next position during data transmission.  
Figure 2 presents the aim of wireless connectivity in this study. When the V2V system uses a nondirectional antenna, the radio transmittable distance between the vehicles is shorter. Further, because of the radio noises from the obstacles or the movements of the vehicles, the real connectivity of the RSSI (Received Signal Strength Indicator) levels becomes unstable, as shown in Figure 2. Thus, the purpose of this study is to provide a longer radio range and stable connectivity in the proposed road surveillance system.  
Therefore, this paper proposed the AAA system with machine learning-based image recognition to realize a longer radio range and quick antenna controls, and the paper also introduced the antenna's angle adjustments via the Kalman Filter algorithm for radio noise reductions and predictions of the driving vehicle's locations.