###### Abstract
To analyze the applicability of communication-based intersection motion planners in real-road situations, a study was conducted assuming that the 5G network is used to construct the vehicle-to-vehicle communication [25]. The motion planner based on the finite state machine was proposed to select the driving strategies by using the information from connected vehicles [26]. The concept of virtual platooning was proposed to provide cooperative intersection control [27]. The optimization-based intersection control system was developed based on vehicle-to-vehicle communication [28]. Many of the studies mentioned so far have assumed that information on the target vehicles is given. To receive the target information from the blind spots, the sensors were installed on the infrastructure [29]. However, obtaining all target information is a difficult assumption in an urban environment. Therefore, a proactive strategy was utilized for urban autonomous driving in previous studies. The trajectory generation and speed control for proactive motion planning was proposed to respond to pedestrians [30] and preceding vehicles [31]. Proactive driving for blind intersections was modeled to provide a smooth and comfortable driving pattern for autonomous vehicles [32; 33]. To define the safe approaching motion, the visibility graph was used to define the possible collision moment in intersections [34]. A game tree-based proactive motion planner was designed and evaluated for a left-turning scenario [35]. Similarly, game-theoretic modeling was used to model negotiation of uncontrolled intersections [36]. Reinforcement learning was applied to provide an efficient strategy for occluded intersections [37; 38; 39; 40; 41]. As described in the previous studies, there have been two categories to develop the motion planner for intersections. The first is developing the motion-planning algorithm for intersection passing based on the given target information. The second is providing the proactive approaching motion for specific scenarios, for example, a left-turning scenario. However, there have been few approaches that designed a proactive motion planner for uncontrolled blind intersections based on the information from environment sensors on the autonomous vehicle and digital maps. Therefore, it is necessary to develop an approach-motion planner for uncontrolled blind intersections and evaluate the proposed algorithm under various driving scenarios in multi-vehicle driving conditions. In this paper, the proactive motion planning algorithm for uncontrolled blind intersections is proposed and evaluated. The proactive motion planner decides the desired longitudinal acceleration of the autonomous vehicle until detecting the target vehicle. After detecting the target vehicles, the interaction motion is decided by using the previous work [42]. The motion planner consists of an approach-motion decision and target state tracking modules. The approach-motion decision module determines the target states of the autonomous vehicle by considering the field of view (FOV) of sensors, calculation delay, and actuator performance. The determined target states are tracked by a longitudinal motion controller using MPC to calculate the longitudinal acceleration input. The effectiveness of the proposed algorithm was evaluated by the Monte Carlo simulation with respect to safety and traffic efficiency. The contributions of this paper are summarized as follows:  
1. The proposed algorithm provides human-like behavior when approaching the uncontrolled intersection to avoid an inevitable collision with targets deriving from blind spots. The concept of the virtual conflict point and the proactive zone is proposed to realize proactive motion planning in uncontrolled intersections. 2. The proactive motion planner is designed based on the FOV of sensors, which is estimated by using the intersection map and static obstacle map. Therefore, the proposed algorithm can utilize various types of sensors capable of constructing a static obstacle map. 3.