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Henry Liu

University of Michigan · Mechanical Engineering

Active 1939–2026

h-index73
Citations17.3k
Papers593120 last 5y
Funding$6.0M1 active

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Professor Henry Liu is a faculty member in the Department of Mechanical Engineering at the University of Michigan, with a joint appointment in Civil & Environmental Engineering. He holds a Ph.D. in Civil and Environmental Engineering from the University of Wisconsin – Madison, obtained in 2000, and a B.S. in Automotive Engineering from Tsinghua University in China, earned in 1993. His research focuses on interdisciplinary areas at the interface between Transportation Engineering, Automotive Engineering, and Artificial Intelligence. Specifically, he is engaged in traffic flow monitoring, modeling, and control, as well as testing and evaluation of connected and automated vehicles, and cooperative automated driving. His scholarly interests include cyber-physical transportation systems, which integrate advanced computational and communication technologies with transportation infrastructure and vehicles to improve traffic efficiency and safety.

Research topics

  • Artificial Intelligence
  • Computer Science
  • Data Mining
  • Reliability engineering
  • Engineering
  • Software engineering

Selected publications

  • Dense reinforcement learning for safety validation of autonomous vehicles

    Nature · 2023-03-22 · 502 citations

    articleSenior authorCorresponding
    PublisherDOI

  • Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment

    Nature Communications · 2021 · 381 citations

    Senior authorCorresponding

    Driving intelligence tests are critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial ad…

    DOI

  • Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology

    IEEE Transactions on Intelligent Transportation Systems · 2020 · 236 citations

    Senior authorCorresponding

    Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and yet there is no systematic framework to generate testing scenario library. This study aims to provide a general framework for the testing scenario library generation (TSLG) problem with different operational design domains (ODDs), CAV models, and performance metrics. Given an ODD, the testing scenario library is defined as a critical set of scenarios that can be used for CA…

    DOI

  • Curse of rarity for autonomous vehicles

    Nature Communications · 2024-06-05 · 65 citations

    articleOpen access1st authorCorresponding

    The curse of rarity—the rarity of safety-critical events in high-dimensional variable spaces—presents significant challenges in ensuring the safety of autonomous vehicles using deep learning. Looking at it from distinct perspectives, we identify three potential approaches for addressing the issue. The curse of rarity—the rarity of safety-critical events in high-dimensional variable spaces—presents significant challenges in ensuring the safety of autonomous vehicles using deep learning. Looking a…

    PublisherOA PDFDOI

  • Optimizing Mixed Traffic Flow: Longitudinal Control of Connected and Automated Vehicles to Mitigate Traffic Oscillations

    IEEE Transactions on Intelligent Transportation Systems · 2025-01-08 · 11 citations

    article

    This paper presents a traffic oscillation mitigation-oriented optimal control framework for connected and automated vehicles (CAVs) in a mixed traffic environment where the behavior of human-driven vehicles (HVs) is unknown. The primary objective of this framework is to alleviate traffic oscillations, thereby improving overall traffic flow. To achieve this, we introduce a novel total equilibrium spacing estimation method, incorporating stochastic parameters into a car-following model and quantif…

    PublisherDOI

Recent grants

Frequent coauthors

  • Yiheng Feng

    91 shared

  • Xuan Di

    Columbia University

    48 shared

  • Shengyin Shen

    Michigan Department of Transportation

    46 shared

  • Jianfeng Zheng

    Changzhou University

    42 shared

  • Xiaozheng He

    41 shared

  • Will Recker

    University of California, Irvine

    41 shared

  • Shuo Feng

    Tsinghua University

    36 shared

  • Wai Wong

    Monash University Malaysia

    34 shared

Education

  • Ph.D., Civil and Environmental Engineering

    University of Wisconsin Madison

    2000

  • B.S., Automotive Engineering

    Tsinghua University

    1993

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