Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics
cs.AI, cs.SE
Submitted: 2026-10-01
Updated: 2026-10-01
Comments: Paper accepted at accepted at IEEE Transactions on Intelligent Transportation Systems. DOI: 10.1109/TITS.2026.3711756
Journal ref: IEEE Transactions on Intelligent Transportation Systems, 2026
DOI: 10.1109/TITS.2026.3711756.
Code: https://github.com/ApolloAuto/apollo
Project page: https://hadoop.apache.org
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
- Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends
- A Multivocal Review of MLOps Practices, Challenges and Open Issues
- A Survey on Text Classification: From Shallow to Deep Learning
- MLOps: A Multiple Case Study in Industry 4.0
- AIOps Solutions for Incident Management: Technical Guidelines and A Comprehensive Literature Review
- A Survey of Large Language Models
- Vision Language Models in Autonomous Driving: A Survey and Outlook
- Strada-LLM: Graph LLM for traffic prediction
- Multimodal LLM for Intelligent Transportation Systems
- Traffic Performance GPT (TP-GPT): Real-Time Data Informed Intelligent ChatBot for Transportation Surveillance and Management
- Ethical and social risks of harm from Language Models
- NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails
- A Comprehensive Overview of Large Language Models
- LoRA: Low-Rank Adaptation of Large Language Models
- Foundation Models for Decision Making: Problems, Methods, and Opportunities
- Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems
- Unleashing the potential of prompt engineering for large language models
- A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT
- A Survey on In-context Learning
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