Toward resilient autonomous driving—An experience report on integrating resilience mechanisms into the Apollo autonomous driving software stack

Abstract

Autonomous driver assistance systems (ADAS) have been progressively pushed to extremes. Today, increasingly sophisticated algorithms, such as deep neural networks, assume responsibility for critical driving functionality, including operating the vehicle at various levels of autonomy. Elaborate obstacle detection, classification, and prediction algorithms, mostly vision-based, trajectory planning, and smooth control algorithms, take over what humans learn until they are permitted to control vehicles and beyond. And even if humans remain in the loop (e.g., to intervene in case of error, as required by autonomy levels 3 and 4), it remains questionable whether distracted human drivers will react appropriately, given the high speed at which vehicles drive and the complex traffic situations they have to cope with. A further pitfall is trusting the whole autonomous driving stack not to fail due to accidental causes and to be robust against cyberattacks of increasing sophistication. In this experience report, we share our findings in retrofitting application-agnostic resilience mechanisms into an existing hardware-/software-stack for autonomous driving—Apollo—as well as where application knowledge helps improve existing resilience algorithms. Our goal is to ultimately decrease the vulnerability of autonomously driving vehicles to accidental faults and attacks, allowing them to absorb and tolerate both, as well as to come out of them at least as secure as before the attack has happened. We demonstrate replication and rejuvenation on the driving stack's Control module and indicate how this resilience can be extended both downwards to the hardware level, as well as upwards to the prediction and planning modules.