## 6 Conclusions
Moreover, in real-world challenges, adverse weather (rain, snow, fog), nighttime driving, and sensor misalignment due to mechanical vibration or calibration drift present additional limitations not yet fully addressed. These conditions may cause significant noise in the LiDAR point cloud data and reduce the effectiveness of camera-based detection. Furthermore, another limitation of our method is that the projected transparent sparse depth map encodes limited depth values, where the 0-pixel code represents 0 m, and the 255-pixel code represents 255 m. This design does not allow for the precise representation of intermediate depth values (such as 0.5 m and 10.5 m), which may increase distance accuracy in certain cases. Future work will focus on resolving these challenges by using different weather-augmented or simulated training data and self-calibrating sensor fusion frameworks to ensure robustness under failure conditions. In addition, future research could explore advanced deep learning techniques to push the limits of sensor fusion. Transformer-based sensor fusion architectures, such as cross-attention for image-LiDAR alignment, offer a promising direction for more effective multi-modal integration. Moreover, incorporating self-supervised learning methods for depth completion can reduce reliance on annotated visual data. Multi-scale graph-based fusion approaches may also offer improvements in spatial reasoning and scene understanding. Finally, extensive evaluations in varied and complex real-world situations, including rural roads, low light, night and darkness conditions, and inclement weather, such as rainy, cloudy, snowy, and foggy conditions, will be essential to confirm the generalizability and robustness of the suggested approaches. Conceptualization, M.R.S. and A.S.Y. ; methodology, A.S.Y. ; software, A.S.Y. ; validation, A.S.Y. ; formal analysis, A.S.Y. ; investigation, A.S.Y. ; resources, A.S.Y. ; data curation, A.S.Y. ; writing--original draft preparation, A.S.Y. ; writing--review and editing, A.S.Y., H.M. and M.R.S. ; visualization, A.S.Y. ; supervision, H.M.; project administration, A.S.Y. ; funding acquisition, A.S.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding and [PERSON]'s Ph.D. is sponsored by the Ministry of National Education of Turkiye. Not applicable. The data used in this study are from the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) Vision Benchmark 2D Object Detection Evaluation 2012 dataset. The dataset can be accessed publicly at [[https://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d](https://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d)]([https://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d](https://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d)) (accessed on 10 January 2022). I would like to thank Aidrivers LTD for providing a server computer equipped with an Intel(r) Xeon(r) 2.20 GHz CPU, 64 GB of RAM, an NVIDIA GeForce RTX 3090 GPU with 24 GB of VRAM, and CUDA 12, running Ubuntu 20.04.6, which greatly contributed to the success of this paper. The authors declare no conflicts of interest.