GASP applies the breakthrough success of large language models' self-supervised training to autonomous driving, creating systems that better understand both physical space and environmental context.

Key innovations:

• Leverages vast amounts of spatiotemporal driving data to learn underlying geometric and semantic structures
• Adopts next-token prediction methodology from LLMs, adapted for autonomous driving contexts
• Creates a unified approach to understand both physical environments and their meaningful interpretation
• Establishes a foundation for more robust and safer autonomous driving systems

This engineering breakthrough matters because it represents a fundamental shift in how autonomous systems learn to perceive and navigate complex environments, potentially accelerating the path to safe, scalable self-driving technology.

GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for Autonomous Driving