mlsanityData to 14 Oct 2026

Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling

arXiv 2411.17826 ↗First posted 26 Nov 2024cs.RO cs.LG stat.MLcited by 3

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Abstract (arXiv)

Ensuring the safety of autonomous vehicles (AVs) requires both accurate estimation of their performance and efficient discovery of potential failure cases. This paper introduces Bayesian adaptive multifidelity sampling (BAMS), which leverages the power of adaptive Bayesian sampling to achieve efficient discovery while simultaneously estimating the rate of adverse events. BAMS prioritizes exploration of regions with potentially low performance, leading to the identification of novel and critical scenarios that traditional methods might miss. Using real-world AV data we demonstrate that BAMS discovers 10 times as many issues as Monte Carlo (MC) and importance sampling (IS) baselines, while at the same time generating rate estimates with variances 15 and 6 times narrower than MC and IS baselines respectively.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 24 of its 58 references that are in the map. First posted on arXiv 26 Nov 2024.

Builds on

24 of 58 references.

References · 24

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12 more
  1. 2020A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systemscited by 71
  2. 2018Constant-Time Predictive Distributions for Gaussian Processescited by 48
  3. 2020Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systemscited by 35
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  7. 2021A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulationcited by 21
  8. 2022Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curriculacited by 17
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  10. 20231001 Ways of Scenario Generation for Testing of Self-driving Cars: A Surveycited by 8
  11. 2020Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systemscited by 6
  12. 2020Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Scenario Generation for Testing of Autonomous Driving Systems Using Real-World Failure Recordscited by 0
  2. 2026Coverage Aware Active Evaluation for Failure Discovery with Paired Systemscited by 0
  3. 2026Careful Judge: Safe and Efficient Human-AI Collaborative Decision Makingcited by 0