WS18: Human- Machine Interface in Intelligent Vehicles
( CFP )

Workshop Code: 9wf53


  • Prof. Zhenghai Gao
    Affiliation: Jilin University

  • Prof. Rencheng Zheng
    Affiliation: Dalian University of Technology

  • Asso. Prof. Kimihiko Nakano
    Affiliation: The University of Tokyo

  • Asso. Prof. Xuewu Ji
    Affiliation: Tsinghua University

  • Prof. Wenbin Hou
    Affiliation: Dalian University of Technology

Scope and Goals

This workshop is launched as a forum for researchers and engineers interested in how people function with intelligent vehicle systems, and we tries to demonstrate new cutting-edge developments and discuss future perspectives through our international research group. This workshop focuses on intelligent vehicle systems, an exciting new research area for human factors. All of us are users of intelligent vehicle as operators, passengers, and customers. Moreover, because the human drivers is the key to successful practice of intelligent vehicle, human factors plays a prominent role in advanced driver assistance systems, automated or connected vehicles.

This workshop covers both practical and theoretical aspects of human factors in intelligent vehicle systems, with an emphasis on their interaction. From a scientific viewpoint, the domain of intelligent vehicle offers an opportunity to create and test sophisticated models of human behavior and cognition. Human factor is a scientific discipline that is concerned with the interaction of people and devices of various kinds. In our case the ‘‘devices’’ with which we are concerned are intelligent vehicle and their operational environment. The primary issues of concern are human abilities and limitations as they related to the intelligent vehicle, including aspects of vehicular and systematic design that might contribute to discomfort or accident. Human factors people carry out research on human performance capability with the intent that the information so obtained be used in the design intelligent vehicle to make them as effective, safe, and easy to use as possible.

We expect the workshop to be useful for professionals in the disciplines of human factors, cognitive science, experimental psychology, sociology, vehicle and transportation engineering. It is also intended to appeal to the specialist in industry, government, or academic, as well as the researchers in need of platform for exchange of new ideas about the interaction between people and complex systems.

Topics of Interest

  • Driver Behavior Analysis for ADAS
  • Driving Experience Analysis for IV
  • Advanced Human-Computer Interaction for IV
  • Cooperating Driving with V2X Communications
  • Driver Automation Collaboration
  • Driver Physiology, Psychology and Sociology

Potential Contributing Authors

  • Professor, Zhenhai Gao, Jilin University,
    Driving Experience Analysis for IV Design

  • Associate Professor, Kimihiko Nakano, The University of Tokyo,
    Haptic Guidance Steering for Intelligent Vehicle

  • Associate Professor, Xuewu Ji, Tsinghua University,
    Application of Game Theory to Investigate Shared Driving behaviors

  • Associate Professor, Lie Guo, Dalian University of Technology,
    Braking Control for Pedetrian Collision Avoidance System Based on the Driver-Vehicle Cooperation Systems

  • Associate Professor, Yunshun Zhang, Jiangxu University,
    Optimal Control Systems for Intelligent Connected Vehcile by Application of V2X Communications

  • Associate Professor, Mingheng Zhang, Dalian University of Technology,
    Driver Fatigue Risk Identification Methods Based on the Improved Hidden Markov Model

  • Associate Professor, Hongyu Hu, Jilin University,
    Yong Driver Behavior Analysis under Anger Emotion

  • Associate Professor, Yibing Zhao, Dalian Universtiy of Technology,
    Control Method for Intelligent Collision Avoidence System Considering Driver and Environmental Factors

  • Associate Professor, Linhui Li, Dalian University of Technology,
    Deep-Learning-Based Stereo Vision to Identify Dynamic Charateristics of Surrounding Pedetrian and Vehicle