Year: 2022
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Hsiao, Yu-Shun, Hari, Siva Kumar Sastry, Filipiuk, Michał, Tsai, Timothy, Sullivan, Michael B., Reddi, Vijay Janapa, Singh, Vasu, Keckler, Stephen W.
Institutions: Nvidia (United States), Harvard University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4229452898