6 citations · 26 across the 17 of their papers we have counts for
19 papers
Hierarchical Control for Continuous-time Systems via General Approximate Alternating Simulation Relations
Zhiyuan Huang, Shuo Li, Murat Arcak +2
This paper introduces a general approximate alternating simulation relation (\emph{-gAAS relation}) for continuous-time systems, which relaxes existing simulation rela…
Propagation of Input Tail Uncertainty in Rare-Event Estimation: A Light versus Heavy Tail Dichotomy
Zhiyuan Huang, Henry Lam, Zhenyuan Liu
We consider the estimation of small probabilities or other risk quantities associated with rare but catastrophic events. In the model-based literature, much of the focus has been d…
Test Against High-Dimensional Uncertainties: Accelerated Evaluation of Autonomous Vehicles with Deep Importance Sampling
Mansur Arief, Zhepeng Cen, Zhenyuan Liu +4
Evaluating the performance of autonomous vehicles (AV) and their complex subsystems to high precision under naturalistic circumstances remains a challenge, especially when failure…
Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems
Mansur Arief, Yuanlu Bai, Wenhao Ding +4
Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events. These techniques often…
Over-Conservativeness of Variance-Based Efficiency Criteria and Probabilistic Efficiency in Rare-Event Simulation
Yuanlu Bai, Zhiyuan Huang, Henry Lam +1
In rare-event simulation, an importance sampling (IS) estimator is regarded as efficient if its relative error, namely the ratio between its standard deviation and mean, is suffici…
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling
Mengdi Xu, Peide Huang, Fengpei Li +6
Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces wher…
Model Calibration via Distributionally Robust Optimization: On the NASA Langley Uncertainty Quantification Challenge
Yuanlu Bai, Zhiyuan Huang, Henry Lam
We study a methodology to tackle the NASA Langley Uncertainty Quantification Challenge, a model calibration problem under both aleatory and epistemic uncertainties. Our methodology…
Rare-Event Simulation for Neural Network and Random Forest Predictors
Yuanlu Bai, Zhiyuan Huang, Henry Lam +1
We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random fo…
A Distributionally Robust Optimization Approach to the NASA Langley Uncertainty Quantification Challenge
Yuanlu Bai, Zhiyuan Huang, Henry Lam
We study a methodology to tackle the NASA Langley Uncertainty Quantification Challenge problem, based on an integration of robust optimization, more specifically a recent line of r…
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems
Mansur Arief, Zhiyuan Huang, Guru Koushik Senthil Kumar +5
Evaluating the reliability of intelligent physical systems against rare safety-critical events poses a huge testing burden for real-world applications. Simulation provides a useful…
Evaluation Uncertainty in Data-Driven Self-Driving Testing
Zhiyuan Huang, Mansur Arief, Henry Lam +1
Safety evaluation of self-driving technologies has been extensively studied. One recent approach uses Monte Carlo based evaluation to estimate the occurrence probabilities of safet…
Synthesis of Different Autonomous Vehicles Test Approaches
Zhiyuan Huang, Mansur Arief, Henry Lam +1
Currently, the most prevalent way to evaluate an autonomous vehicle is to directly test it on the public road. However, because of recent accidents caused by autonomous vehicles, i…
A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods
Zhiyuan Huang, Yaohui Guo, Henry Lam +1
Evaluation and validation of complicated control systems are crucial to guarantee usability and safety. Usually, failure happens in some very rarely encountered situations, but onc…
Towards Affordable On-track Testing for Autonomous Vehicle - A Kriging-based Statistical Approach
Zhiyuan Huang, Henry Lam, Ding Zhao
This paper discusses the use of Kriging model in Automated Vehicle evaluation. We explore how a Kriging model can help reduce the number of experiments or simulations in the Accele…
An Accelerated Testing Approach for Automated Vehicles with Background Traffic Described by Joint Distributions
Zhiyuan Huang, Henry Lam, Ding Zhao
This paper proposes a new framework based on joint statistical models for evaluating risks of automated vehicles in a naturalistic driving environment. The previous studies on the…
Sequential Experimentation to Efficiently Test Automated Vehicles
Zhiyuan Huang, Henry Lam, Ding Zhao
Automated vehicles have been under heavy developments in major auto and tech companies and are expected to release into market in the foreseeable future. However, the road safety o…
Learning-based Robust Optimization: Procedures and Statistical Guarantees
L. Jeff Hong, Zhiyuan Huang, Henry Lam
Robust optimization (RO) is a common approach to tractably obtain safeguarding solutions for optimization problems with uncertain constraints. In this paper, we study a statistical…
Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models
Zhiyuan Huang, Ding Zhao, Henry Lam +1
The process to certify highly Automated Vehicles has not yet been defined by any country in the world. Currently, companies test Automated Vehicles on public roads, which is time-c…
Evaluation of Automated Vehicles in the Frontal Cut-in Scenario - an Enhanced Approach using Piecewise Mixture Models
Zhiyuan Huang, Ding Zhao, Henry Lam +2
Evaluation and testing are critical for the development of Automated Vehicles (AVs). Currently, companies test AVs on public roads, which is very time-consuming and inefficient. We…