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Yiheng Feng

Yiheng Feng

· Associate Professor of Civil and Construction Engineering and Assistant Director of the Center for Road Safety (CRS)

Purdue University · Civil and Construction Engineering

Active 1998–2026

h-index38
Citations5.5k
Papers15474 last 5y
Funding$300k

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

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About

Yiheng Feng is an Associate Professor of Civil and Construction Engineering and serves as the Assistant Director of the Center for Road Safety (CRS) at Purdue University. His research focuses on civil engineering topics related to road safety, infrastructure, and transportation systems. As a faculty member, he contributes to advancing knowledge in these areas through his academic and research activities, supporting the development of safer and more efficient transportation environments.

Research topics

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

Selected publications

  • Evaluation of an Infrastructure-Based Warning System: A Case Study on Roundabout Driving Behaviors

    IEEE Transactions on Intelligent Transportation Systems · 2025-03-14 · 4 citations

    article

    Smart intersections have the potential to improve road safety with sensing, communication, and edge computing technologies. Perception sensors installed at a smart intersection can monitor the traffic environment in real-time and send infrastructure-based warnings to nearby travelers through vehicle-to-everything (V2X) communication. This study investigated how infrastructure-based warnings can influence driving behaviors and improve roundabout safety through a driving simulator experiment. A co…

    PublisherDOI

  • Vehicle-Group-Based Crash Risk Prediction and Interpretation on Highways

    IEEE Transactions on Intelligent Transportation Systems · 2025-04-09 · 3 citations

    article

    Previous studies in predicting crash risks primarily associated the number or likelihood of crashes on a road segment with traffic parameters or geometric characteristics, usually neglecting the impact of vehicles’ continuous movement and interactions with nearby vehicles. Recent technology advances, such as Connected and Automated Vehicles (CAVs) and drones, are able to collect high-resolution trajectory data, which enable trajectory-based risk analysis. This study investigates a new vehicle gr…

    PublisherDOI

  • A Dynamic Unmanned Aerial Vehicle Routing Framework for Urban Traffic Monitoring

    ArXiv.org · 2025-01-16 · 2 citations

    preprintOpen accessSenior author

    Unmanned Aerial Vehicles (UAVs) have great potential in urban traffic monitoring due to their rapid speed, cost-effectiveness, and extensive field-of-view, while being unconstrained by traffic congestion. However, their limited flight duration presents critical challenges in sustainable recharging strategies and efficient route planning in long-term monitoring tasks. Additionally, existing approaches for long-term monitoring often neglect the evolving nature of urban traffic networks. In this st…

    PublisherOA PDFDOI

  • On-Board Vision-Language Models (VLMs) for Personalized Motion Control of Autonomous Vehicles

    2025-10-19 · 1 citations

    article

    Personalized driving refers to an autonomous vehicle’s ability to adapt its driving behavior or control strategies to match individual users’ preferences and driving styles while maintaining safety and comfort standards. However, existing works either fail to capture every individual’s preference precisely or become computationally inefficient as the user base expands. Vision-Language Models (VLMs) offer promising solutions to this front through their natural language understanding and scene rea…

    PublisherDOI

  • Highway Workers’ Perception of Autonomous Truck-Mounted Attenuator: A Case Study in Indiana’s DOT

    2025-06-05 · 1 citations

    articleCorresponding

    Highway maintenance operations have been associated with high rates of crashes, leading to workers’ injuries and fatalities. To protect the state Department of Transportation (DOT)’s maintenance workers, autonomous systems have been implemented in various work zone maintenance operations, including the Autonomous Truck-Mounted Attenuator (ATMA). Though the ATMA has been tested in several DOTs, DOT workers’ perception of the ATMA has not been well studied. To understand workers’ perception of the…

    PublisherDOI

Recent grants

Frequent coauthors

  • Henry Liu

    91 shared

  • Larry Head

    University of Arizona

    47 shared

  • Mehdi Zamanipour

    Federal Highway Administration

    27 shared

  • Shayan Khoshmagham

    27 shared

  • Z. Morley Mao

    University of Michigan–Ann Arbor

    21 shared

  • Qi Alfred Chen

    21 shared

  • Shuo Feng

    Tsinghua University

    20 shared

  • Chunhui Yu

    Tongji University

    19 shared

Education

  • Ph.D., Systems and Industrial Engineering

    University of Arizona

    2015

Awards & honors

  • Civil Engineering Alumni Achievement Award
  • Construction Engineering Outstanding and Emerging Leader Alu…
  • LSCCE Distinguished Engineering Alumni

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