Abstract-Fully autonomous vehicles (AVs), i.e., AVs with autonomy level 5, are expected to dominate road transportation in the near-future and contribute trillions of dollars to the global economy. The general public, government organizations, and manufacturers all have significant concern regarding resiliency and safety standards of the autonomous driving system (ADS) of AVs . In this work, we proposed and developed (a) 'Kayotee' -a fault injection-based tool to systematically inject faults into software and hardware components of the ADS to assess the safety and reliability of AVs to faults and errors, and (b) an ontology model to characterize errors and safety violations impacting reliability and safety of AVs. Kayotee is capable of characterizing fault propagation and resiliency at different levels -(a) hardware, (b) software, (c) vehicle dynamics, and (d) traffic resilience. We used Kayotee to study a proprietary ADS technology built by Nvidia corporation and are currently applying Kayotee to other open-source ADS systems. The safety and reliability of autonomous vehicles (AVs) are significant concerns among all the stakeholders. Our previous work [1] characterized a California Department of Motor Vehicles (DMV) dataset on reported AV road testing and showed that as many as 36% of disengagements were caused by computer system problems and 64% were due to machine learning problems. AV research has traditionally focused on improving machine learning and artificial intelligence models. However, as these models are deployed at large scale on computing platforms, the focus is to assess the resilience and safety features of the compute stack driving the AVs. The effects of faults and errors in the hardware (GPUs, CPUs and other processing units) running the AV software stack is not well understood. Recent work [2]-[5] exclusively focus on the resiliency of deep neural networks (DNNs) to hardware faults and errors without accounting for the inherent resiliency in the software stack. [6], [7] study the safety of AVs by injecting sensor-related permanent faults such as Gaussian noise, occlusion, etc. into publicly available autonomous driving system (such as CARLA [8] and Open Pilot [9]). However, these autonomous driving system (ADS) are overly simplistic with few sensors and are not representative of a production ADS. Moreover, such studies have limited scope as they cannot characterize error masking and propagation of transient, and permanent faults in the ADS. We believe our work is the first to study the impact of transient errors, intermittent errors and permanent errors (with some limitations) on AV safety and reliability.
Multiple recent works are trying to assess the safety of neural networks, especially targeting autonomous driving systems REF .
Kayotee: A Fault Injection-based System to Assess the Safety and Reliability of Autonomous Vehicles to Faults and Errors
ArXiv
ArXiv
Computer Science