Autonomous Solutions Inc. (ASI) has developed an algorithm that allows autonomous vehicles to better detect “negative obstacles” and occlusions, blockages that prevent sensor data gathering. While their previous obstacle detection and avoidance platforms focused on “positive” obstacles above-ground, the new algorithm extends that detection to hazards such as cliffs, berm edges and drop-offs. Taylor Bybee, perception tech lead at ASI, explained that the algorithm “complements the existing obstacle detection and avoidance system by providing additional context for the vehicle to make decisions.” The algorithm relies on point cloud sensors such as LiDAR, structured light or stereo cameras to model its field of view. ASI’s new software module is available in current ASI automation kits containing point cloud sensors with no additional hardware needed and is capable of integration into other systems.