## 1 Introduction  
The vehicle has been a most widely used form of transportation in people's lives over the last few decades, and various studies were consequently proposed for road safety. Passive safety technologies are designed to mitigate the effects of traffic accidents, and have been undergoing a lot of development. Nowadays, due to the increasing use of electronics in the automobile industry, it is now also using active safety technologies. Note that the difference between passive and active safety is the operating time--namely, an active safety system will operate before the accident and thus attempt to avoid such accidents [1]. Many advanced safety techniques have been developed for various scenarios, such as the Lane Departure Warning system (LDW) [2], Forward Collision Warning system (FCW) [3], Blind Spot Warning system (BSW) [4], and Parking Assistant System (PAS) [5; 6; 7], and navigation [8; 9]. Therefore, vehicular safety systems have already become more and more indispensable for up-to-date vehicles. Among those techniques, Internet of Vehicles (IoV) is one of the most interesting and important technologies to the modern automobile industry, where it is clearly a moving network made up of the Internet of Things (IoT). A vehicle can communicate with other vehicles and infrastructures through the Internet for various applications. It enables vehicles to perform more effective managements, such as in collision avoidance systems, navigation, intelligent transport systems, and entertainment systems. Communication between vehicles and traffic infrastructures has been studied extensively [1; 10; 11; 12; 13]. In [10], the transmission of information in urban intersections was analyzed and evaluated. In order to improve its performance, the authors proposed a vehicle-assisted relaying scheme, where the relaying vehicle is selected in an autonomous manner. Moreover, since collision avoidance with multiple data resource reservation per schedule assignment is a critical issue for the improvement of broadcast reliability, the authors in [11] proposed an enhanced method to address it. Their simulation results show that by adopting their design, the network capacity in terms of supported vehicles under given service requirements is largely increased. Recently, a comprehensive survey on resource allocation schemes, including Dedicated Short-Range Communications (DSRC) and cellular networks, was proposed in [12]. On the other hand, IoV-based systems have been exploited in different traffic applications, such as collision avoidance. In [14], the Inertial Measurement Unit (IMU), DSRC, vehicle dynamics, computer vision, and Global Positioning System (GPS) were combined to improve both the accuracy and reliability of the vehicle's positioning system. Unfortunately, the above studies are under the assumption that all vehicles in the scenario should have some form of communication ability. It is too difficult to meet such an assumption during the development of IoV. Therefore, making up for such a deficiency is currently a significant topic in IoV-based safety. Consequently, in [1], the authors proposed a form of collision-avoidance architecture based on computer vision, machine learning, vehicle dynamics, and the predictive algorithm. By adding some extra equipment, their architecture is able to eliminate the assumption that all vehicles in the considered scenario should have communication ability. Consider the following scenario: if there is an infrastructure at the intersection, which can observe every vehicle from each direction and predict whether the collision will happen or not, and then warn vehicles via communication ability and/or the traffic sign, the collision avoidance system will still work even if some vehicles are without communication ability. In particular, the authors designed a linear algorithm based on the output of an existing real-time object detection project, YOLOv3 [15; 16], to predict the future position of a vehicle.