Sce2DriveX transforms how MLLMs (Multimodal Large Language Models) handle autonomous driving by converting scene understanding into precise vehicle control commands.

• Integrates semantic understanding with motion control in a unified framework
• Creates human-like driving behaviors that generalize across different traffic scenarios
• Addresses the critical challenge of translating high-level perception into low-level vehicle actions
• Demonstrates an engineering breakthrough in end-to-end autonomous systems

This research represents a significant advancement in Embodied AI for autonomous vehicles, potentially improving safety and performance in real-world driving conditions.

Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning