A joint calibration framework for roadside sensors--such as camera, Light Detection and Ranging (LiDAR), millimeter wave radio detection and ranging (radar), and fused sensors--is defined by this standard. The types of sensors involved (including their characteristics and measurement data features), the reference coordinate system, the calibration process, and the tools for calibrating the sensors are defined by this standard. Additionally, a data analysis method to assess the accuracy and reliability of the calibration results is defined by this standard.
- Standard Committee
- C/DC - Data Compression Standards Committee
- Status
- Active PAR
- PAR Approval
- 2023-06-05
Working Group Details
- Society
- IEEE Computer Society
- Standard Committee
- C/DC - Data Compression Standards Committee
- Working Group
-
DFAD - Data framework for autonomous driving
- IEEE Program Manager
- Meng Zhao
Contact Meng Zhao - Working Group Chair
- Yanyong Zhang
Other Activities From This Working Group
Current projects that have been authorized by the IEEE SA Standards Board to develop a standard.
P3184
Standard for Data Framework for Autonomous Driving
This standard defines a data-related architecture for highly efficient autonomous driving systems. The architectural components, interfaces, transmission protocols and connection protocols are specified. Definitions of efficiency for autonomous driving systems are provided, including metrics for accuracy of perception and decision-making, the latency of the required computations, as well as the energy consumption of the sensors and computing units.
P3184.2
Standard for Property Graph Description of Road Networks
A property graph description of high-quality road network data, such as lanes, roads, and intersections are defined in this standard. Lanes, roads, and intersections within a road network are depicted as nodes, while the connections and relationships between these elements are illustrated by edges in the graph. This standard also defines properties of graph nodes and edges, as well as data types.
P3184.3
Standard for Software-in-the-Loop Simulation Testing of Planning and Control Modules in Autonomous Driving Systems
This standard provides general testing guidelines and scenario generation frameworks for Software-in-the-Loop (SiL) simulation testing of planning and control (PnC) modules in autonomous driving systems (ADS). It defines terminologies related to SiL simulation testing for the PnC module and outlines a data generation framework for testing. Specifically, this standard specifies types and formats of input scenarios, general testing methods, the scope and format of output data, and quantitative evaluation metrics for the testing.
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