## 2 Related Work  
There have been several works which implement DS-based multisource data fusion for autonomous vehicle safety. In [28], a method for calculating the probabilities of existence of nearby objects was proposed. From the local sensing results at ego-vehicle, the existence probability is obtained by the DS fusion. A trajectory selection problem was considered in [29], which proposed a decision-making algorithm based on multicriteria such as safety, comfort, and energy consumption. The DS is used for risk assessment, which combines multiple local risk levels obtained from the predefined multiple criteria. However, as their schemes rely only on ego-vehicle's information, it has limitations on collisions predictions. In [30,31], collision avoidance schemes based on occupancy grid for autonomous vehicle were considered. The state of cells in occupancy grid is determined by the DS that combines the Light Detection And Ranging (LiDAR) data. In [32], the authors proposed a real-time object detection algorithm. The DS is used to fuse the features extracted from sparse point clouds, and experimental results showed good efficiency in the urban traffic environments. However, most of these works focus on the classical DS combination scheme, which may result in erroneous output when the input confidences are highly conflicting.  
To overcome the shortcoming of classical DS dealing with conflicting confidences, several distance metrics for evidence and improved DS fusion schemes were proposed. The metric called the Jaccard index, which is based on the cardinalities of the union and intersection of two subsets, is widely used [24]. In [27], a new distance measure which is more sensitive to change of evidence was presented. A pignistic probability transformation based distance measures were studied in [33,34]. Despite many attempts, distance metrics accounting for the asymmetry in the importance of sensed events were not fully studied. To incorporate new distance metrics in DS, modified DS fusion methods were proposed. In [25], a new weighted DS combination scheme was proposed where weights are calculated by distance measure. Moreover, in order to manage confidence conflicting, weighted combination schemes based on conventional distance with uncertainty [26] or ambiguity measures [35,36] were also proposed. However, the above state-of-the-art DS fusion schemes use a distance metric which assumes the evidences are of equal importance. Therefore, it is difficult to apply those fusion methods directly to the autonomous vehicle environment in which the evidences and related events potentially differ in their importance.  
In [37], collision avoidance at intersections was studied with real-world experiments. V2V communication is used to share each vehicle's information such as velocity, and then vehicle trajectories are estimated by Kalman filter. Based on trajectory estimation, each vehicle controls its velocity to avoid collision. In [38], a system for cooperative collision avoidance for overtaking scenario was proposed. For forward (rear-end) collision avoidance, the work in [5] uses deep learning, and the authors of [39] presented a collision avoidance scheme considering the measurement uncertainty in sensors. Although there exist many studies on collision prediction and avoidance, in-depth studies on data fusion for cooperative sensing in autonomous vehicles are necessary.