## 1 Introduction  
There has been a rapid advancement in the development of sensing systems for autonomous driving that has notably elevated the effectiveness of perception tasks, such as object detection, in recent years. Despite these achievements in research and development, there remains a lack of widespread adoption of level 4 or 5 autonomous driving capabilities in commercial vehicles due to autonomous vehicles (AVs)' reliance on single-sensor perception of the real world and a substantial commitment to research and development to guarantee the continuous evolution and enhancement in the technology over time [1]. Furthermore, the prediction and decision--making processes in AVs that rely on a single sensor can be hampered by external factors such as bad weather, occlusion, or poor lighting conditions because cameras struggle in low-light environments, whereas radars cannot detect objects with rich visual features. This limitation in cameras and radar, and the potential consequences of reliance on a single sensor for object detection in AVs, has generated significant attention in the field of research toward the utilization of multi-modal based sensing in the automotive domain, especially in perception systems that fuse camera and radar inputs [2; 3; 4]. An ideal fusion system utilizing both the cameras and radar sensor information can effectively leverage the advantages of both these sensors, concurrently addressing the limitations inherent in each. While a camera as a sensor input offers detailed texture and semantic information, its performance diminishes with long-range small objects, occlusions, and poor lighting conditions, whereas radar as a sensor input exhibits the ability to offer reliable performance in all weather and lighting conditions, detect small objects at long ranges, and operate without hindrance from issues related to occlusions. However, radar sensors encounter difficulties in precisely identifying objects due to the absence of detailed texture and semantic features [2; 5; 6]. The work presented within this paper revolves around the primary objective of determining how we can effectively harness the benefits of both modalities (camera and radar sensors) to attain precise and dependable object detection. An optimal Camera-Radar fusion network must capitalize on the advantages offered by both sensors. Simultaneously, it should also guarantee that the limitations of one sensor do not impact the performance of the other. Previous studies in the fusion of camera and radar modalities have often employed the mapping of radar data onto the camera's data [7]. However, working within this technique imposes limitations on performance, particularly in scenarios involving object occlusion, thus resulting in inefficient use of radar sensor data. More sophisticated and cutting-edge methodologies engage in fusion at the feature level instead of directly mapping features. For instance, the approach proposed for the AVOD network [8] extracts bird-eye view features concurrently from the camera and the radar's sensor input. This approach then merges these features on a per-object basis to capitalize on the unique information extracted by each of these modalities to perform camera-radar sensor fusion. However, it is observed that the concurrent approach of extracting features and performing fusion does not hold true for instances where the camera sensor becomes unreliable in situations such as occluded objects or adverse conditions, like rain or fog. In such scenarios, radar-based sensors are not affected and work well, but the reliability of camera-based sensors can significantly decrease, resulting in a notable overall performance decline in the AV system for object detection in such adverse conditions. Evidently, there is a necessity to enhance the dependability of camera-radar systems to attain satisfactory performance, particularly in situations where the quality of camera input is compromised due to external factors.