## 4 Experiments and Results
### Visualization of Samples
While LiDAR can meticulously map the immediate surroundings, radar's wider net can detect vehicles or obstacles further down the road, providing crucial information for safe navigation. This complementary perspective offered by radar, despite its sparser data, paints a more comprehensive picture of the environment, enhancing perception and decision-making capabilities in scenarios demanding long-range awareness. Figure 12: An example of Camera-Radar fusion-based 3D object detection at an intersection using the proposed NeXtFusion network. Here, blue bounding boxes, denote people, yellow bounding boxes denote cars and red bounding boxes denote bus. Figure 13: An illustration of radar point-cloud data of the environment from the nuScenes dataset generated by NeXtFusion. Dots represent objects, with darker shades indicating closer proximity to the sensor. Figure 14 illustrates a point-cloud plot generated by combining radar data from five sweeps. This data is obtained from the nuScenes dataset utilized for experiments outlined in this paper. As seen in this plot, radar detection lines visually align with their respective bounding boxes of two vehicles, providing an estimate of the detected objects. Figure 15 shifts the evaluation and visualization focus to object tracking, for which a tracking pipeline was developed for evaluation purposes. By providing more reliable starting points in each frame, the proposed network analyzes data about objects, which, in other words, are individual entities within the environment that an autonomous vehicle (AV) needs to detect, track, and potentially interact with based on the conditions. Examples include specific vehicles, pedestrians, traffic signs, or other relevant objects. Understanding these instances and their associated metadata is crucial for safe and efficient navigation. Consider a hypothetical scenario where the camera sensor is affected by lens glare from the sun rays directly hitting the camera sensor, resulting in an occlusion in the field of vision. Here, an AV encounters pedestrians crossing an intersection on the road. Examining the instance metadata associated with this object unveils valuable information beyond its simple presence, even in adverse conditions such as lens glare. These rich metadata are generated by fusing information from radar and cameras in NeXtFusion, which plays a critical role in AV perception and decision making. By tracking and analyzing instance metadata, the AV can accomplish the following:  
Figure 14: An example of very confident radar returns from two vehicles, captured and combined using five radar sweeps. Light blue lines indicate radar detection, indicating its length and orientation. Red cross indicates vehicle’s ego-centric position. Here, blue bounding boxes, denote people, yellow bounding boxes denote cars and red bounding boxes denote bus. * Continuously monitor the movement of objects in its environment. * Classify and differentiate between different types of objects and understand their potential intentions even under unfavorable conditions. * Make informed decisions by planning safe maneuvers based on perceived information about the environment. While Figure 15 serves as a static representation to understand how the proposed network performs 3D object detection and tracking, the AV perception is always dynamic in nature and performs object detection and tracking for each frame. The work presented within this paper focuses on processing individual camera images and radar scans simultaneously. Also, metadata continuously updates as sensors gather new information, allowing the AV to adapt its understanding of the surrounding world in real time. This dynamic interplay between sensor data, object detection and tracking, and rich metadata forms the foundation for safe and intelligent navigation for autonomous driving.