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Zhiyuan Huang - IEEE Xplore Author Profile

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Author details

Zhiyuan Huang

Publications

14

Citations

140

Publications by Year

20172025

Co-Authors:

Mansur AriefZhepeng CenYaohui GuoPeide HuangXianfeng Huang

Show All Co-Authors (17)

Zhiyuan Huang

Affiliation

School of Economics and Management

Tongji University

Shanghai, China

Publication Topics

Autonomous Vehicles,
Confidence Interval,
Confidence Interval Width,
Exponential Distribution,
Input Uncertainty,
Kullback-Leibler,
Probability Estimates,
Adaptive Sampling,
Anomaly Data,
Autonomous Vehicle Perception,
Back Propagation Neural Network,
Beginning Of Episode

Biography

Zhiyuan Huang is currently pursuing the Ph.D. degree in industrial and operations engineering with the University of Michigan, Ann Arbor. His research interests include simulation and stochastic optimization.(Based on document published on 21 November 2017).

Publications

14

Citations

140

Publications by Year

20172025

Co-Authors:

Mansur Arief
Zhepeng Cen
Yaohui Guo
Peide Huang
Xianfeng Huang

Show All Co-Authors (17)

Author's Published Works

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Showing 1-14 of 14 results

Conferences (12)

Journals (2)

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Single Year
Zhiyuan Huang(14)
Henry Lam(13)
Ding Zhao(12)
Mansur Arief(4)
David J. LeBlanc(2)
Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA(4)
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA(3)
Department of Industrial Engineering and Operations Research, Columbia University, New York, NY, USA(3)
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA(3)
Department of Industrial and Operations Engineering, University of Michigan, MI, USA(2)
2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)(2)
2018 Winter Simulation Conference (WSC)(2)
2017 IEEE International Conference on Robotics and Automation (ICRA)(1)
2017 Winter Simulation Conference (WSC)(1)
2018 21st International Conference on Intelligent Transportation Systems (ITSC)(1)
IEEE(14)
Gothenburg, Sweden(2)
Yokohama, Japan(2)
Auckland, New Zealand(1)
Kyoto, Japan(1)
Las Vegas, NV, USA(1)
Lane Change(7)
Automated Vehicles(6)
Rare Events(5)
Autonomous Vehicles(4)
Distribution Models(4)

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Results

Multivariate Long-Term Forecasting Using Multilinear Trend Fuzzy Information Granules for Traffic Time Series

Xianfeng Huang;Zhiyuan Huang;Jianming Zhan

IEEE Transactions on Fuzzy Systems

Year: 2025 | Volume: 33, Issue: 3 | Journal Article |

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Long-term forecasting for time series is gaining significant attention in many emerging fields, such as machine learning and artificial intelligence. Linear fuzzy information granulation is a well-recognized and powerful tool for long-term forecasting. However, existing studies on this topic have been restricted to univariate time series, falling short of meeting the real-world demands of complex ...Show More

Multivariate Long-Term Forecasting Using Multilinear Trend Fuzzy Information Granules for Traffic Time Series

Xianfeng Huang;Zhiyuan Huang;Jianming Zhan

IEEE Transactions on Fuzzy Systems

Year: 2025 | Volume: 33, Issue: 3 | Journal Article |

Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling

Mengdi Xu;Peide Huang;Fengpei Li;Jiacheng Zhu;Xuewei Qi;Kentaro Oguchi;Zhiyuan Huang;Henry Lam;Ding Zhao

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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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 where limited scalability dictates a prohibitively large number of testing iterations. On the other hand, a biased or inaccurate policy evaluation in a safety-critical system could potentially cause unexpected catastrophic f...Show More

Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling

Mengdi Xu;Peide Huang;Fengpei Li;Jiacheng Zhu;Xuewei Qi;Kentaro Oguchi;Zhiyuan Huang;Henry Lam;Ding Zhao

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Year: 2022 | Conference Paper |

Certifiable Evaluation for Autonomous Vehicle Perception Systems using Deep Importance Sampling (Deep IS)

Mansur Arief;Zhepeng Cen;Zhenyuan Liu;Zhiyuan Huang;Bo Li;Henry Lam;Ding Zhao

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2022 | Conference Paper |

Cited by: Papers (4)

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Evaluating the performance of autonomous vehicles (AV) and their complex AI-driven functionalities to high precision under naturalistic conditions remains a challenge, especially when the failure or dangerous cases are rare. Rarity does not only require an enormous sample size for a naive method to achieve high confidence residual risk estimation, but it can also cause serious risk underestimation...Show More

Certifiable Evaluation for Autonomous Vehicle Perception Systems using Deep Importance Sampling (Deep IS)

Mansur Arief;Zhepeng Cen;Zhenyuan Liu;Zhiyuan Huang;Bo Li;Henry Lam;Ding Zhao

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2022 | Conference Paper |

On The Impacts of Tail Model Uncertainty in Rare-Event Estimation

Zhiyuan Huang;Henry Lam

2019 Winter Simulation Conference (WSC)

Year: 2019 | Conference Paper |

Cited by: Papers (1)

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Rare-event probabilities and risk measures that quantify the likelihood of catastrophic or failure events can be sensitive to the accuracy of the underlying input models, especially regarding their tail behaviors. We investigate how the lack of tail information of the input can affect the output extremal measures, in relation to the level of data that are needed to inform the input tail. Using the...Show More

On The Impacts of Tail Model Uncertainty in Rare-Event Estimation

Zhiyuan Huang;Henry Lam

2019 Winter Simulation Conference (WSC)

Year: 2019 | Conference Paper |

Evaluation Uncertainty in Data-Driven Self-Driving Testing

Zhiyuan Huang;Mansur Arief;Henry Lam;Ding Zhao

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

Year: 2019 | Conference Paper |

Cited by: Papers (11)

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Safety evaluation of self-driving technologies has been extensively studied. One recent approach uses Monte Carlo based evaluation to estimate the occurrence probabilities of safety-critical events as safety measures. These Monte Carlo samples are generated from stochastic input models constructed based on real-world data. In this paper, we propose an approach to assess the impact on the probabili...Show More

Evaluation Uncertainty in Data-Driven Self-Driving Testing

Zhiyuan Huang;Mansur Arief;Henry Lam;Ding Zhao

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

Year: 2019 | Conference Paper |

DESIGNING IMPORTANCE SAMPLERS TO SIMULATE MACHINE LEARNING PREDICTORS VIA OPTIMIZATION

Zhiyuan Huang;Henry Lam;Ding Zhao

2018 Winter Simulation Conference (WSC)

Year: 2018 | Conference Paper |

Cited by: Papers (3)

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We study the problem of designing good importance sampling (IS) schemes to simulate the probability that a sophisticated predictor, built for instance from an off-the-shelf machine learning toolbox, gives a prediction that exceeds a large threshold. This problem is motivated as a step towards building good learning algorithms that takes into account the extremal risks of the prediction. We provide...Show More

DESIGNING IMPORTANCE SAMPLERS TO SIMULATE MACHINE LEARNING PREDICTORS VIA OPTIMIZATION

Zhiyuan Huang;Henry Lam;Ding Zhao

2018 Winter Simulation Conference (WSC)

Year: 2018 | Conference Paper |

RARE-EVENT SIMULATION WITHOUT STRUCTURAL INFORMATION: A LEARNING-BASED APPROACH

Zhiyuan Huang;Henry Lam;Ding Zhao

2018 Winter Simulation Conference (WSC)

Year: 2018 | Conference Paper |

Cited by: Papers (6)

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Importance sampling has been extensively studied as a variance reduction tool in rare-event simulation. The design and efficiency of this method often relies on structural knowledge about the target problem in hand. In this paper, we consider a simple rare-event setting, driven by Gaussian input variates, where there is no direct information about the system of interest, or where the system of int...Show More

RARE-EVENT SIMULATION WITHOUT STRUCTURAL INFORMATION: A LEARNING-BASED APPROACH

Zhiyuan Huang;Henry Lam;Ding Zhao

2018 Winter Simulation Conference (WSC)

Year: 2018 | Conference Paper |

Synthesis of Different Autonomous Vehicles Test Approaches

Zhiyuan Huang;Mansur Arief;Henry Lam;Ding Zhao

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

Year: 2018 | Conference Paper |

Cited by: Papers (8)

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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, it becomes controversial about whether on-road tests should be the best approach. Alternatively, people use test tracks or simulation to assess the safety of autonomous vehicles. These approaches are time-efficient and le...Show More

Synthesis of Different Autonomous Vehicles Test Approaches

Zhiyuan Huang;Mansur Arief;Henry Lam;Ding Zhao

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

Year: 2018 | Conference Paper |

A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods

Zhiyuan Huang;Yaohui Guo;Mansur Arief;Henry Lam;Ding Zhao

2018 Annual American Control Conference (ACC)

Year: 2018 | Conference Paper |

Cited by: Papers (16)

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Evaluation and validation of complicated control systems are crucial to guarantee usability and safety. Usually, failure happens in some very rarely encountered situations, but once triggered, the consequence is disastrous. Accelerated Evaluation is a methodology that efficiently tests those rarely-occurring yet critical failures via smartly-sampled test cases. The distribution used in sampling is...Show More

A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods

Zhiyuan Huang;Yaohui Guo;Mansur Arief;Henry Lam;Ding Zhao

2018 Annual American Control Conference (ACC)

Year: 2018 | Conference Paper |

Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models

Zhiyuan Huang;Henry Lam;David J. LeBlanc;Ding Zhao

IEEE Transactions on Intelligent Transportation Systems

Year: 2018 | Volume: 19, Issue: 9 | Journal Article |

Cited by: Papers (39)

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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-consuming and inefficient. We proposed the accelerated evaluation concept, which uses a modified statistics of the surrounding vehicles and the importance sampling theory to reduce the evaluation time by several orders of...Show More

Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models

Zhiyuan Huang;Henry Lam;David J. LeBlanc;Ding Zhao

IEEE Transactions on Intelligent Transportation Systems

Year: 2018 | Volume: 19, Issue: 9 | Journal Article |

An accelerated testing approach for automated vehicles with background traffic described by joint distributions

Zhiyuan Huang;Henry Lam;Ding Zhao

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2017 | Conference Paper |

Cited by: Papers (14)

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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 Accelerated Evaluation for automated vehicles are extended from multi-independent-variate models to joint statistics. The proposed toolkit includes exploration of the rare event (e.g. crash) sets and construction of acce...Show More

An accelerated testing approach for automated vehicles with background traffic described by joint distributions

Zhiyuan Huang;Henry Lam;Ding Zhao

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2017 | Conference Paper |

Towards affordable on-track testing for autonomous vehicle — A Kriging-based statistical approach

Zhiyuan Huang;Henry Lam;Ding Zhao

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2017 | Conference Paper |

Cited by: Papers (12)

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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 Accelerated Evaluation procedure. We also propose an adaptive sampling scheme for selecting samples to construct the Kriging model. Application examples in the lane change scenario are presented to illustrate the proposed meth...Show More

Towards affordable on-track testing for autonomous vehicle — A Kriging-based statistical approach

Zhiyuan Huang;Henry Lam;Ding Zhao

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2017 | Conference Paper |

Sequential experimentation to efficiently test automated vehicles

Zhiyuan Huang;Henry Lam;Ding Zhao

2017 Winter Simulation Conference (WSC)

Year: 2017 | Conference Paper |

Cited by: Papers (6)

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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 of these vehicles remains a concern. One approach to evaluate their safety is via on-track experimentation, but this requires gigantic costs and time investments. This paper discusses a sequential learning approach based ...Show More

Sequential experimentation to efficiently test automated vehicles

Zhiyuan Huang;Henry Lam;Ding Zhao

2017 Winter Simulation Conference (WSC)

Year: 2017 | Conference Paper |

Evaluation of automated vehicles in the frontal cut-in scenario — An enhanced approach using piecewise mixture models

Zhiyuan Huang;Ding Zhao;Henry Lam;David J. LeBlanc;Huei Peng

2017 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2017 | Conference Paper |

Cited by: Papers (19)

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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 proposed the Accelerated Evaluation concept which uses a modified statistics of the surrounding vehicles and the Importance Sampling theory to reduce the evaluation time by several orders of magnitude, while ensuring th...Show More

Evaluation of automated vehicles in the frontal cut-in scenario — An enhanced approach using piecewise mixture models

Zhiyuan Huang;Ding Zhao;Henry Lam;David J. LeBlanc;Huei Peng

2017 IEEE International Conference on Robotics and Automation (ICRA)

Year: 2017 | Conference Paper |

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