In autonomous driving and advanced driver-assistance systems (ADAS), radar sensors play a crucial role in detecting and tracking objects under diverse environmental conditions [1, 2, 3]. One persistent challenge is the accurate representation of long vehicles, such as trucks and buses, whose extended radar signatures often result in a wide spatial spread of detections with similar radial velocities, making association difficult due to gating limitations. The GRID component processes raw radar measurements to generate occupancy grids and extract candidate targets, including stationary environmental structures [4, 5]. In the case of stationary long vehicles, multiple stationary objects may overlap with grid-based environment targets, allowing them to be consolidated into a single tracked object. This paper presents a novel approach for radar object merging and retention decision-making using fuzzy logic to distinguish between true moving and stationary objects in the presence of GRID targets and object association mechanisms. The association modules link radar measurements or detections, when available, with existing tracks to maintain temporal consistency, enabling the formation of radar objects represented by track points. The proposed method fuses radar objects based on their radial velocity and spatial overlap, while a fuzzy logic–based decision engine evaluates object persistence. The approach improves object continuity and reduces false positives in driving functions such as Blind Spot Detection (BSD), Emergency Brake Assist (EBA), and Adaptive Cruise Control (ACC). Simulation and real-world validation results demonstrate significant improvements in object tracking accuracy, particularly for long vehicles in urban environments.