## 1 Introduction  
Autonomous vehicles have been considered as a key technology for intelligent transportation systems. According to the research by National Highway Traffic Safety Administration (NHTSA) in 2015, human error causes around 94% traffic crashes [1], and autonomous vehicles are expected to reduce traffic crashes drastically by minimizing drivers' mistakes [2]. The design of operating software as well as diagnostics tool for autonomous driving have been actively investigated [3]. In particular, a proper anti-collision system is one of the most crucial components of autonomous vehicle systems. The information including location, distance between adjacent vehicles, and the vehicle velocity can be obtained by the equipped sensors such as radar, Global Positioning System (GPS), and cameras. Collision avoidance systems based on sensing data are actively studied [4, 5], including systems to fuse the sensing data obtained from multiple sensors, e.g., combining data from radar and cameras [6]. However, autonomous vehicle systems relying solely on ego-vehicle sensors have limitations in dealing with the collisions due to reasons such as bad weather, sensor malfunctioning, or blind spots, etc. [7, 8, 9]. The risk can be reduced by utilizing sensed information from the neighboring vehicles through Vehicle-to-Vehicle (V2V) communication [10, 11] such as Dedicated Short Range Communications (DSRC) [12] which can support safety applications in high data rates [13, 14]. In order to leverage sensed data from multiple vehicles in real-time, we require a data fusion system with low complexity and high reliability. In multisensor systems, there is uncertainty caused by inaccuracy of sensors or complicated sensing environment [15]. In this respect, the \"Dempster-Shafer theory of evidence (DS)\" [16; 17] is a widely used evidence theory to comprehensively fuse multisource information including uncertainty. It does not require any priori information and requires a simple calculation where the outcome can be reinforced as more data become available [18; 19; 20]. Although DS has many advantages, the classical DS combination may yield unreasonable outputs when evidences are highly conflicting [18; 19; 20]. This is critical for autonomous vehicle safety, i.e., vehicles may have erroneous sensing results. Data fusion can cause either false positive or false negative errors. The former is a false alarm, i.e., the vehicle falsely identifies a nonexisting object as an existing one. The latter represents misdetection of an existing object or obstacles. In the context of autonomous vehicle safety, false negatives are significantly dangerous [21] according to the case studies on autonomous vehicle accidents [22]. Therefore, we would like to put more emphasis on the confidence in existence rather than nonexistence of objects, and this necessity results in an asymmetry in the importance of sensed events. However, most of existing works do not consider this aspect. Although there are many works which incorporate the degree of conflict into a fusion method [23; 24; 25; 26; 27], the studies on evidence distance taking the aforementioned issue into account are rare. In this paper, we develop a novel metric for evidence distance to measure conflict by assigning asymmetric weights to sensed results and propose a real-time data fusion scheme based on the new metric. In addition, we outline a V2V communication method for anti-collision systems in which the proposed data fusion scheme can be effectively applied. We summarize the main contributions of this paper as follows. 1. _A new metric for evidence distance for real-time data fusion._ We propose a novel metric for autonomous vehicle to measure distance between confidences on detected events of asymmetric importance. Next, we propose real-time data fusion scheme based on the new distance, which combines multiple sensing data from adjacent vehicles.