Think of this as putting a very smart co-pilot brain next to the traditional self-driving software. Classic autonomous driving systems are good at seeing and controlling the car, but they’re narrow and rigid. Large AI models add a ‘common sense’ layer that can understand complex road situations, follow natural-language instructions, and coordinate with humans and other systems more flexibly.
Traditional autonomous driving stacks struggle with long‑tail edge cases, complex reasoning (e.g., multi‑agent interactions, ambiguous road rules), and natural human-machine interaction. Applying large AI models aims to reduce disengagements and accidents, speed up adaptation to new environments, and enable richer supervision, simulation, and decision support for self-driving systems.
Tight integration of large models with proprietary driving datasets, perception stacks, HD maps, and simulation environments; plus safety validation tooling and regulatory approvals, which make the combined system and data very hard to replicate quickly.
Hybrid
Vector Search
High (Custom Models/Infra)
Training and inference cost for large models under real-time latency and automotive-grade safety constraints (on-vehicle compute, bandwidth limits, and certification requirements).
Early Adopters
Focus on systematically integrating large general-purpose AI models into multiple layers of the autonomous driving stack (perception, planning, simulation, HMI) rather than treating them as a bolt-on assistant, with emphasis on reasoning for edge cases and complex multi-agent traffic scenarios.