V2X-QA: A Comprehensive Reasoning Dataset and Benchmark for Multimodal Large Language Models in Autonomous Driving Across Ego, Infrastructure, and Cooperative Views

1. 1Collect and annotate a comprehensive dataset of real-world driving scenarios
2. 2Develop a benchmark to evaluate multimodal large language models across vehicle-side, infrastructure-side, and cooperative viewpoints
3. 3Use V2X-QA to assess model performance and identify areas for improvement
4. 4Fine-tune models using V2X-QA to enhance their reasoning capabilities in autonomous driving

AI engineers and researchers working on autonomous driving projects can benefit from V2X-QA to evaluate and improve their models' performance in various driving conditions

💡 V2X-QA provides a comprehensive evaluation framework for multimodal large language models in autonomous driving, covering ego, infrastructure, and cooperative viewpoints

🚗💻 Introducing V2X-QA: a dataset and benchmark for evaluating multimodal large language models in autonomous driving #AI #AutonomousDriving