Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 15 references that are in the map. First posted on arXiv 6 May 2022.
Builds on
7 of 15 references.
References · 7
- 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
- 2019MLPerf Inference Benchmarkcited by 195
- 2019Multiple Futures Predictioncited by 178
- 2017On a Formal Model of Safe and Scalable Self-driving Carscited by 129
- 2021PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulationcited by 29
- 2021RoboRun: A Robot Runtime to Exploit Spatial Heterogeneitycited by 3
- 2018Domain Specific Approximation for Object Detectioncited by 1
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.
3 papers.