Braum: Analyzing and Protecting Autonomous Machine Software Stack

Autonomous machines, such as Autonomous Vehicles (AV), are vulnerable to a variety of different faults such as radiation-induced soft/transient errors, adversarial attacks, and software bugs, which all jeopardize the reliability of autonomous machines. How vulnerable the AV software stack is to different error sources, however, remains an open question. This paper performs comprehensively fault injections to study how the AV software stack behaves under different error sources. We show that algorithms in an AV software stack inherently possess different forms of masking mechanisms. Based on the characteristic of the inherent fault tolerance mechanisms, we formalize the notion of Fault Tolerance Level (FTL), which quantifies how faults in an algorithm can be masked and/or attenuated without affecting the actuator commands, providing opportunities to relax fault protection. Leveraging the FTL formulation, we propose a dynamic protection system, which, at the high level, spends the limited protection budget (e.g., spatial/temporal redundancy) on the most vulnerable parts of the AV software (i.e., with the lowest FTL). Using Autoware as a case study, we show that our system reduces the error rate of AV software stack by more than 90% with negliaible performance overhead.