Predictable Data-Driven Resource Management: an Implementation using Autoware on Autonomous Platforms

Autonomous embedded systems (AES) are becoming prominent in many application domains such as self-driving cars. However, the conflict between the rather limited memory space in such systems and the data intensive nature of the workloads creates hard challenges on data and memory management, which may easily cause unpredictability in outputting autonomous control decisions. In this paper, we target data-driven AES featuring the integrated architecture by establishing a data-centric system model inspired by Heijunka, a mature production leveling methodology developed by Toyota. Based on this new model, we develop ResCue which contains a dynamic data scheduler and a flexible memory reservation scheme to ensure both temporal and spatial data availability, which shall guarantee predictability in generating outputs in terms of both meeting deadlines and minimizing jitters. We implement and extensively evaluate ResCue under various settings using a popular end-to-end self-driving software Autoware on top of the AES-specific NVIDIA AGX Xavier SoC. Results show that ResCue never misses a deadline and yields a maximum jitter of merely 834 microseconds, while incurring rather small overhead. Moreover, ResCue is able to noticeably reduce memory consumption compared to vanilla Autoware.