  Vision-based autonomous driving requires reliable and efficient object
detection. This work proposes a DiffusionDet-based framework that exploits data
fusion from the monocular camera and depth sensor to provide the RGB and depth
(RGB-D) data. Within this framework, ground truth bounding boxes are randomly
reshaped as part of the training phase, allowing the model to learn the reverse
diffusion process of noise addition. The system methodically enhances a
randomly generated set of boxes at the inference stage, guiding them toward
accurate final detections. By integrating the textural and color features from
RGB images with the spatial depth information from the LiDAR sensors, the
proposed framework employs a feature fusion that substantially enhances object
detection of automotive targets. The $2.3$ AP gain in detecting automotive
targets is achieved through comprehensive experiments using the KITTI dataset.
Specifically, the improved performance of the proposed approach in detecting
small objects is demonstrated.
