Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles

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The processing requirement of autonomous vehicles (AVs) for high-accuracy perception in complex scenarios can exceed the resources offered by the in-vehicle computer, degrading safety and comfort. This paper proposes a sensor frame processing rate (FPR) estimation model, Zhuyi, that quantifies the minimum safe FPR continuously in a driving scenario. Zhuyi can be employed post-deployment as an online safety check and to prioritize work. Experiments conducted using a multi-camera state-of-the-art industry AV system show that Zhuyi’s estimated FPRs are conservative, yet the system can maintain safety by processing only 36% or fewer frames compared to a default 30-FPR system in the tested scenarios.


Yu-Shun Hsiao (Harvard University)
Michał Filipiuk (NVIDIA)
Timothy Tsai (NVIDIA)
Vijay Janapa Reddi (Harvard University)
Vasu Singh (NVIDIA)

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