Selective Symbolic Type-Guided Checkpointing and Restoration for Autonomous Vehicle Repair

Abstract

Fault tolerant design can help autonomous vehicle systems address defects, environmental changes and security attacks. Checkpoint and restoration fault tolerance techniques save a copy of an application's state before a problem occurs and restore that state afterwards. However, traditional Checkpoint/Restore techniques still admit high overhead, may carry along tainted data, and rarely operate in tandem with human-written or automated repairs that modify source code or alter data layout. Thus, it can be difficult to apply traditional Checkpoint/Restore techniques to solve the issues of non-environmental defects, security attacks or software bugs. To address such challenges, in this paper, we propose and evaluate a selective checkpoint and restore (SCR) technique that records only critical system state based on types and minimal symbolic annotations to deploy repaired programs. We found that using source-level symbolic information allows an application to be resumed even after its code is modified in our evaluation. We evaluate our approach using a commodity autonomous vehicle system and demonstrate that it admits manual and automated software repairs, does not carry tainted data, and has low overhead.