  Combining LiDAR and camera data has shown potential in enhancing
short-distance object detection in autonomous driving systems. Yet, the fusion
encounters difficulties with extended distance detection due to the contrast
between LiDAR's sparse data and the dense resolution of cameras. Besides,
discrepancies in the two data representations further complicate fusion
methods. We introduce AYDIV, a novel framework integrating a tri-phase
alignment process specifically designed to enhance long-distance detection even
amidst data discrepancies. AYDIV consists of the Global Contextual Fusion
Alignment Transformer (GCFAT), which improves the extraction of camera features
and provides a deeper understanding of large-scale patterns; the Sparse Fused
Feature Attention (SFFA), which fine-tunes the fusion of LiDAR and camera
details; and the Volumetric Grid Attention (VGA) for a comprehensive spatial
data fusion. AYDIV's performance on the Waymo Open Dataset (WOD) with an
improvement of 1.24% in mAPH value(L2 difficulty) and the Argoverse2 Dataset
with a performance improvement of 7.40% in AP value demonstrates its efficacy
in comparison to other existing fusion-based methods. Our code is publicly
available at https://github.com/sanjay-810/AYDIV2
