## 3 Target State Decision and Tracking
### Target State Decision for Approach Motion  
The goal of proactive motion planning is to improve safety by predicting possible risky situations and taking a driving strategy to respond to risky situations. In the case of autonomous driving on uncontrolled blind intersections, the possibility of target vehicles darting out from blind spots is the primary risk even for human drivers. Since the perception system of the autonomous vehicle is relatively more vulnerable to blind spots than humans, the proactive driving strategy is important for carrying out autonomous driving in urban environments. For example, a laser scanner cannot detect the objects behind the  
Figure 3: Example intersection for map building. closest object on the line of the laser beam. This approach is similar to the driving pattern of experienced drivers who reduce the risk by decelerating in advance in the presence of blind spots. The first stage in deciding the target state for a proactive approach motion is predicting the crossing point with vehicles darting out from the boundary of the FOV estimate. The crossing point is named the 'virtual conflict point' in this study. This is because this point is not an actual conflict point with the detected target, but a possible conflict point. The estimation of sensor FOV is activated after an autonomous vehicle driving in the 'free driving zone' enters the 'proactive zone'. The transition position from the free driving zone to the proactive zone is determined based on the nominal braking distance of the approaching autonomous vehicle. In this study, we use a \(-2.0\) m/s\({}^{2}\) as a nominal deceleration to determine the transition position. After an autonomous vehicle enters the proactive zone, FOV is estimated based on the size of the intersection region, the specification of the environment sensors, and the static obstacle map from the perception algorithm [43]. The purpose of the FOV estimation is to find the boundary of the blind spots. Thus, the static obstacle map, by using an occupancy grid, is the most important input of the target state decision. That is, if only the static obstacle map can be constructed, various types of environmental sensors can be used. For example, the FOV estimate can be defined as shown in Figure 4. Based on the FOV estimate and path information from the intersection map, all possible dart-out positions are selected, marked with a red cross in Figure 4. The driving paths from the darting out positions are depicted as red solid lines. The virtual conflict points are defined as intersection points between the driving paths of the autonomous vehicle and possible darting-out vehicles, which are shown in Figure 4 as a blue cross mark. The target states for intersection approach motion are determined based on the possible dart-out position and virtual conflict point. The example scenario to explain the target state decision process is depicted in Figure 5, which shows one example of the candidates. First, \(d_{dart}\) is defined as the remaining distance to the virtual conflict point in the view of the approaching target vehicle. The remaining time \(t_{dart}\) to arrive at the virtual conflict point of the target vehicle is calculated by assuming the dart-out velocity \(v_{dart}\) of the target vehicle and dividing the \(d_{dart}\) by \(v_{dart}\). \(v_{dart}\) is defined as estimated traffic flow velocity [44]. \(t_{dart}\) is the remaining time to decelerate the autonomous vehicle to avoid a collision with an approaching target from the blind spots. The target velocity \(v_{target}\) and braking distance \(d_{pmk}\) can be calculated by considering the maximum deceleration \(a_{x,\min}\), processing time of the autonomous driving algorithm \(t_{pmk}\), actuation delay \(t_{act}\), and s  
Figure 4: Virtual conflict point decisions for intersection driving. velocity profile \(v(t)\) of the approach motion can be determined based on the uniform jerk motion during slew time and uniform acceleration motion after reaching \(a_{x,\min}\) as follows:  
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