Volunteers Wanted for Autonomous Vehicles Committee

14 May, 2020 | Member Updates
Article by Shuqing Zeng, VTS Ad Hoc Committee on Autonomous Vehicles Chair
The VTS Ad Hoc Committee on Autonomous Vehicles is seeking volunteers to fill positions as Vice-Chair and Members. Any current IEEE or VTS member may apply.

Selected volunteers will initiate related projects within VTS that focus on autonomous vehicles, and take other actions to enhance professional development amongst VTS members with respect to autonomous vehicles.

Two projects currently in discussion are:

  • Survey paper of autonomous vehicles for the IEEE Vehicular Technology Magazine
  • Guest-edited issue for the IEEE Transactions on Vehicular Technology

About the VTS Ad Hoc Committee on Autonomous Vehicles

Recent breakthroughs in artificial intelligence, sensor, connected vehicle, and electrification technologies come together to get autonomous vehicles (AV) driving safely and efficiently.

However, the reported fatal accidents involving vehicles with AV systems have raised public concerns on safety of AVs. If the robustness and maturity of state-of-the-art autonomous vehicle systems is not further improved, the full potential of autonomous vehicles cannot be achieved.

The VTS Ad Hoc Committee on Autonomous Vehicles is tasked to identify unsolved problems and invite research in the technical aspects of automated driving.

The Committee considers methods and representative techniques to solve typical problems or to document successful test cases and applications. The addressed modalities of the system may range from ego self-contained vehicles to ad-hoc networked platoons, and they may be designed as multi-vehicle systems assisted by cloud computing as well as edge computing.

Key topics include (but are not limited to):

  • Architectures for intelligent autonomous systems
  • Validation and verification of autonomous systems
  • End-to-end driving (generate actuator commands directly from sensory input)
  • Collected vehicles
  • Vehicle-to-vehicle communication
  • AV assisted by edge computing
  • Advanced driver-assistance systems (ADAS)
  • Sensors and hardware (e.g., mono-camera, stereo-camera, omnidirectional camera, automotive radars, LiDARs, multiple camera array)
  • Active sensing
  • Localization and mapping
  • GPS-IMU positioning fusion
  • Simultaneous localization and mapping
  • Visual odometry
  • A priori map-based localization
  • Digital map construction and change management
  • Perception and multi-modality sensor fusion
  • Presentation learning with multi-modality sensors
  • Object detection
  • Semantic segmentation
  • 3D object detection from 3D point cloud
  • Object tracking and re-identification
  • Lane detection and tracking
  • Drivable space identification and plan
  • Threat assessment and situation analysis
  • Uncertainty decision making
  • Vehicle maneuver and behavior planning
  • Path and motion trajectory planning
  • Vehicle dynamics modeling
  • Advanced model-predicted vehicle control
  • Reinforcement learning and its application in autonomous driving
  • Multi-task reinforcement learning
  • Vehicle simulation and use scenario generation
  • Semi-automated image or point clouding annotation system
  • Physical-based photometric modelling and radiometric modelling in simulation

Biography—Shuqing Zeng (M’03–SM’12) received a PhD in Computer Science from Michigan State University in 2004. Since then, he has been with the General Motors Research and Development Center, where he is currently a Staff Researcher. His research interests include computer vision, sensor fusion, autonomous driving, and vehicle active-safety applications. He was a team member of Tartan Racing who won the 2007 DARPA Urban Challenge. He designed the perception system of the semi-autonomous system Super Cruise, first deployed in the 2018 Cadillac CT6.

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