mlsanityData to 7 Oct 2026

Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles

arXiv 2205.03347 ↗First posted 6 May 2022cs.AI cs.ROcited by 3

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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

  1. 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
  2. 2019MLPerf Inference Benchmarkcited by 195
  3. 2019Multiple Futures Predictioncited by 178
  4. 2017On a Formal Model of Safe and Scalable Self-driving Carscited by 129
  5. 2021PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulationcited by 29
  6. 2021RoboRun: A Robot Runtime to Exploit Spatial Heterogeneitycited by 3
  7. 2018Domain Specific Approximation for Object Detectioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.

3 papers.