## 5 Conclusions  
This paper introduces NeXtFusion, a novel deep camera-radar fusion network designed to enhance the perception capabilities of autonomous vehicles (AVs). By effectively combining the strengths of camera and radar data, NeXtFusion overcomes the limitations  
Figure 15: A static representation to analyze how the proposed network performs multi-modal 3D object detection and tracking as part of the validation tests. nuScenes provides data for a scene captured from camera and radar sensors mounted on the AV as follows: (**a**) Front left camera identifies and starts tracking the object. In this example, only a single pedestrian is being tracked for analysis purposes. (**b**) Front center camera continues to track the identified object while it is in its field of view. (**c**) Front right camera continues to track the identified object until it leaves its field of view.  
of single-sensor networks, particularly in challenging weather and lighting conditions. Utilizing an attention module, NeXtFusion extracts crucial features from both modalities, leading to improved object detection accuracy and tracking.  
Extensive evaluations on the nuScenes dataset demonstrate NeXtFusion's superior performance, achieving a significant mAP score improvement compared to existing methods. Additionally, strong performance in other metrics like mATE and mAOE further highlights its overall effectiveness in 3D object detection. Visualizations of real-world data processed by NeXtFusion showcase its ability to handle diverse scenarios. By leveraging the complementary information from cameras and radars, NeXtFusion offers a robust solution for navigating complex environments and ensuring reliable operation under various conditions.  
However, recent research explores the potential of utilizing additional sources of information to enhance an AV's awareness of its perceived surroundings, including but not limited to roadside sensors, such as surveillance cameras and radars, along with unmanned aerial vehicles (UAVs). These technologies contribute to the topic of digitalization of traffic scenes, providing more comprehensive information about the surrounding environment to the AVs. By establishing communication between AVs and this intelligent infrastructure, researchers further envision a future where object detection is not just reliant only on onboard sensors but off-vehicle sensors as well.  
The research work and findings presented in this paper, particularly the ability to combine and extract meaningful and complementary information from camera and radar sensors, could potentially contribute to the development of such infrastructure-vehicle cooperation systems. Exploring this exciting potential application is a promising avenue for future research.  
Conceptualization, P.K. and M.E.-S.; methodology, P.K.; software, P.K.; validation, P.K. and M.E.-S.; writing--original draft preparation, P.K.; writing--review and editing, P.K. and M.E.-S.; supervision, M.E.-S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The openly available public dataset nuScenes has been utilized in this study. It is cited as [35] of this paper. The authors would like to acknowledge the Indiana University Pervasive Technology Institute for providing supercomputing and storage resources as well as the Internet of Things (IoT) Collaboratory at the Purdue School of Engineering and Technology at Indiana University Purdue University at Indianapolis that have contributed to the research results reported within this paper. The authors declare no conflicts of interest.