This research explores how multimodal large language models can overcome current limitations in autonomous driving systems by enhancing their ability to understand and respond to complex driving environments.

• Introduces a specialized Virtual Question Answering dataset to fine-tune LLMs for driving scenarios
• Focuses on improving safety and efficiency in autonomous vehicles
• Addresses critical performance limitations of current autonomous systems
• Combines visual and language processing to enhance vehicle decision-making

For engineering teams, this represents a significant advancement in how AI systems can be applied to solve complex real-world problems, potentially transforming the autonomous vehicle industry with more reliable, adaptable driving systems.

Application of Multimodal Large Language Models in Autonomous Driving