This standard provides requirements to enable effective and reliable application of artificial intelligence (AI) in the field of vehicular traffic engineering. It covers the whole process of model application, including data collection, model training, model deployment, model monitoring and updating used in vehicular traffic engineering. At the same time, this standard covers scenarios of applications of deep learning models, including: - Defining the legitimacy and quality requirements of data sources and clarifying methods and procedures for data collection and analysis processing - Describing model training objectives, methods, and evaluation indicators to help enable that a model performance can achieve the expected results - Clarifying the environment and platform requirements of model inference performance and deployment to ensure that a model can run stably in practical applications - Establishing a monitoring mechanism of model performance - Monitoring a running state and performance index of a model in real time. This standard applies to specific business scenarios such as traffic engineering planning and design, construction management, and operation management. It includes explaining the background, requirements, and characteristics of each application scenario, and explains the specific role and value of deep learning models in scenarios such as improving production efficiency, reducing costs, and optimizing user experience.
- Standard Committee
- CIS/SC - Standards Committee
- Status
- Active PAR
- PAR Approval
- 2024-09-26
Working Group Details
- Society
- IEEE Computational Intelligence Society
- Standard Committee
- CIS/SC - Standards Committee
- Working Group
-
P3478 - P3478 WG for Intelligence for Traffic Engineering (CIS/SC/ITE)
- IEEE Program Manager
- Malia Zaman
Contact Malia Zaman - Working Group Chair
- Jian Wang
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