Self-driving or autonomous vehicles consist of software and hardware subsystems that perform tasks like sensing, perception, path-planning, vehicle control, and actuation. An error in one of these subsystems may manifest itself in any subsystem to which it is connected. Errors in sensor data propagate through the entire software pipeline from perception to path planning to vehicle control. However, while a small number of previous studies have focused on the propagation of errors in pose estimation or image processing, there has been little prior work on systematic evaluation of the propagation of errors through the entire autonomous architecture. In this work, we present a simulation study of error propagation through an autonomous system and work toward developing appropriate metrics for quantifying the error at both the subsystem and system levels. Finally, we demonstrate how the framework for analyzing error propagation can be applied to analysis of an autonomous systems with a lidar-based sensing and perception system.