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)
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)
Localization and mapping
GPS-IMU positioning fusion
Simultaneous localization and mapping
A priori map-based localization
Digital map construction and change management
Perception and multi-modality sensor fusion
Presentation learning with multi-modality sensors
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.