## 2 Overall Architecture of Proactive Motion Planner  
This study focuses on the design and evaluation of a proactive motion planner for uncontrolled blind intersections on urban roads. The objective of this study is to determine the desired longitudinal acceleration of the autonomous vehicle to improve safety and flow efficiency when passing uncontrolled blind intersections. The overall architecture of the proactive motion planner is described in Figure 1. As shown in Figure 1, the upper modules were utilized to provide the information for the proposed algorithm. In this study, vehicle filter, localization, and target detection module were used to obtain the states of the autonomous vehicle and surrounding targets. The route planner and intersection map were utilized to acquire the global driving route of the autonomous vehicle and the possible driving route of other vehicles. The information from the upper modules and sensors was used to configure the algorithm. The proposed algorithm consists of three modules, a driving mode decision, a target state decision, and an MPC-based target state tracker. The driving mode decision algorithm estimates the FOV of the sensors and checks the detection of approaching targets from other branches of the intersection. The target states for the approach and interaction modes are determined by the target state decision module using the concepts of the virtual conflict point. Then, the target states are used as reference states for MPC to obtain the desired longitudinal acceleration. As mentioned in Section 1, many pieces of research have been carried out to develop the motion-planning algorithm for urban autonomous driving. Especially, motion planner for intersection passing has been studied recently. However, the conventional safety parameters, such as clearance (\(C\)), \(TTC\), headway time, or time gap are hard to utilize for intersection driving scenarios, because the paths of the approaching vehicles are crossed inside the intersection, and the driving directions are opposite each other. In order to overcome the limitations of the conventional safety parameters, the safety parameters are defined as shown in Figure 2. \(d_{DTI}\) means the remaining distance to enter the intersection. \(d_{DTC}\) means the distance to the conflict point, which is defined as a cross point between driving routes for host and target vehicles. In this study, the summation of the \(d_{DTC\_{Inst}}\) and \(d_{DTC\_{target}}\) are used to define \(C\). Based on the \(d_{DTI}\) and \(d_{DTC}\), \(C\), time to intersection (\(TTI\)), and \(TTC\) for intersection motion planner is calculated as follows:  
\[ \begin{split}& TTI=\frac{d_{DTI}}{\upsilon_{x}}\\ & C=d_{DTC\_{Inst}}+d_{DTC\_{target}}\\ & TTC=\frac{d_{DTC\_{Inst}}}{\upsilon_{x,host}}+\frac{d_{DTC\_{ target}}}{\upsilon_{x,host}}\end{split}  \tag{1}\]  
Equation (1) considers the host and target vehicle states with the shape of the intersection. In order to estimate the possible driving path of the approaching targets, an intersection map is required to provide the available maneuvers of each driving lane. An example of the four-way intersection is shown in Figure 3. The center line of the road is marked with a solid yellow line, and the lane marker is marked with a black dotted line in Figure 3. As shown in Figure 3, the entry and exit nodes of each driving lane are marked as red and blue circles, respectively. The indices of the nodes are assigned a number from 1 to 8. The driving paths inside the intersection are virtually generated using the B-spline to smoothly connect the entry and exit nodes. The example driving paths from entering  
Figure 1: The overall architecture of the proactive motion planner for uncontrolled blind intersections. Figure 2: Parameter definition for intersection motion planning. nodes 1 and 2 are depicted as a magenta and cyan solid line. Connectivity between each node is defined using the connectivity matrix \(C_{con}\), as follows:  
\[C_{con}=