Occ-LLM represents a breakthrough in autonomous driving technology by combining large language models with 4D occupancy prediction for enhanced vehicle understanding and planning.

• Introduces Motion Separation Variational Autoencoder (MS-VAE) to effectively encode occupancy data for LLM processing
• Enables more accurate prediction of moving vs. static objects in complex driving scenarios
• Delivers improved self-ego planning capabilities through better environmental understanding
• Demonstrates significant performance gains in anticipating traffic movements and responding safely

This innovation matters for engineering because it bridges the gap between language understanding and spatial-temporal reasoning, creating more robust autonomous systems that can better interpret and react to real-world driving conditions.

Occ-LLM: Enhancing Autonomous Driving with Occupancy-Based Large Language Models