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

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.