## 1 Introduction
In May 2016, the camera system of a Tesla Model S failed to distinguish the white side of a tractor trailer against the bright sky, and the vehicle hit the side of the trailer and passed underneath it, resulting in the first case of a traffic fatality involving automated vehicle technology [16]. In March 2018, the lane-keeping system of a Tesla Model X steered into a gore area and crashed into an impact attenuator when the perception system failed to recognize faded lane markings [17]. A crash incident that is very similar to the 2016 Tesla Model S crash occurred in March 2019, where a Tesla Model 3 crashed into the side of a tractor trailer and then drove beneath the trailer [18]. An Uber test vehicle was involved in a crash that resulted in a pedestrian fatality in March 2018, where the vehicle hit a pedestrian who was pushing a bicycle across the road at night [19]. The crash was predictable and avoidable, but a series of design flaws in the perception system contributed to the fatal outcome. There has been increasing interest in connected and automated vehicles (CAVs) in recent years due to its potential to improve road safety, convenience, and energy efficiency [20; 21; 22]. The full benefits of automated driving can only be achieved when vehicles are capable of communicating and exchanging information with each other and also with the surrounding infrastructure. Vehicular wireless communications, which is often referred to as vehicle-to-everything (V2X) communications, incorporates various types of communication options depending on the participating entities. Some of the well-known types include vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-network (V2N) communications. With V2X communications, it is possible to overcome the functional and environmental limitations of the on-board sensors. The advantages of V2X communications in terms of road safety and how it can complement and extend the perception methods based on automotive remote sensors have been described in more detail in [23]. The majority of recent studies on CAVs focus on cooperative adaptive cruise control, cooperative intersection control, and cooperative perception [22]. Although these studies present promising applications, not many studies have examined the benefits of the fusion of on-board remote sensors and cooperative approaches in the context of safety applications. An object association method based on V2V communications and on-board sensors was presented in [24], where the relative sender position and orientation were determined by using point matching algorithms. In [25], the plausibility checking of V2V communication data was implemented based on a multiple object tracking system with a camera sensor. Based on their evaluation, the authors reported that the proposed approach can overcome spoofing attacks if ghost vehicles are located within the camera field-of-view (FOV). A fusion approach based on radar and V2V communication data was suggested in [26], where an improved perception range and more accurate position and velocity estimates were obtained in a car-following scenario. In [27], an object matching algorithm based on V2V communication messages and radar measurements was presented and tested in highway driving scenarios, and the authors suggested that the track-to-track association algorithm can reliably handle the ambiguity issue in object matching. A simulation-based study on cooperative vehicle positioning was presented in [28], where average position errors were calculated for a varying percentage of the vehicles equipped with ranging sensors and V2V communication devices. A high-level fusion approach based on multiple on-board sensors and V2V/V2P communications was presented in [23] for the purpose of providing a timely warning prior to a possible collision.