In autonomous driving systems, advanced sensing technologies (such as Light Detection and Ranging (LIDAR) devices and cameras) can capture high volume of data for real-time traversability analysis. Off-road autonomy is more challenging than other autonomous applications due to the highly unstructured environment with various types of vegetation. The understory with unknown density can create extremely challenging scenarios (such as negative obstacles masked by dense vegetation) by concealing potential obstacles in the terrain, leading to severe vehicle damage, significant financial loss, and even operator injury or death. This paper investigates the impact of understory vegetation density on obstacle detection in off-road traversability analysis. By leveraging a physics-based autonomous driving simulator, a machine learning–based framework is proposed for obstacle detection based on point cloud data captured by LIDAR. It is observed that the increase in the density of understory vegetation adversely affects the classification performance in correctly detecting solid obstacles. With the cumulative approach used in this paper, however, sensitivity results for different density levels converge as the vehicles incorporates more time frame data into the classification algorithm.