Autonomous driving is a rapidly growing sector that is attracting increasing attention from industry and academia. The rise of deep learning techniques has made it possible for autonomous vehicles to perceive the environment around them, including detecting objects of interest around the vehicle. The technology used to detect objects is quite dependent on sensors such as cameras, radars, and LiDARs, which are commonly used in autonomous vehicles for 3D object detection. Current state-of-the-art 3D object detection methods often use a fusion of LiDAR and camera features. As such, the perception abilities of autonomous vehicles are susceptible to sensor corruptions, whether the corruptions are caused by internal sensor faults or extreme environmental conditions. A particularly hazardous corruption occurs when a LiDAR does not record points in certain regions, which can be caused, among other reasons, by dirt accumulation on the LiDAR, a wet ground, and dark-colored objects, which can absorb LiDAR beams or reflect them away from the LiDAR. Corruptions to the LiDAR can be very dangerous as they contribute significantly to the ability of multi-modal 3D object detectors, and the robustness of multi-modal 3D object detection methods against such corruptions is understudied. In this thesis, I propose Ada-UniBEV, which detects missing laser beam returns for the LiDAR based on its geometric pattern of shooting beams, and determines a severity of corruption based on the number of missing beams. Camera features corresponding to the corrupted region of the LiDAR are weighed as more important, with the exact weight being determined as a function of the severity of corruption. The weights also vary spatially around the autonomous vehicle depending on the properties of each spatial location. Ada-UniBEV improves the 3D object detection performance over state-of-the-art UniBEV in moderate to high-severity LiDAR corruption scenarios involving missing points while maintaining the same performance on clean data.