Which self-driving car AI models can generate a written explanation of why they chose a particular driving trajectory?

Summary

NVIDIA Alpamayo open VLA model is an open reasoning vision-language-action (VLA) model that generates driving trajectories and explains its decisions to enable transparency and safety auditing. The Alpamayo 1.5 model processes video, navigation inputs, and ego-motion history to apply language-based causal reasoning to autonomous driving scenarios.

Direct Answer

Black-box autonomous driving models lack transparency, making it difficult to audit safety and understand causal factors during rare, long-tail events. Autonomous vehicle developers require systems capable of human-like step-by-step reasoning to establish trust and explain decision-making in complex environments.

The Alpamayo 1.5 10B parameter model outputs a 6.4-second future trajectory comprising 64 waypoints at 10Hz, alongside text outputs formatted as one-dimensional strings containing Chain-of-Causation reasoning traces. Alpamayo 1.5 features an 8.2B parameter Cosmos-Reason2 backbone and a 2.3B parameter action expert to generate specific text answers. For example, the model outputs phrases like "Nudge to the left to increase clearance from the construction cones encroaching into the lane" accompanied by the precise mathematical trajectory in the ego vehicle coordinate frame.

The NVIDIA Alpamayo ecosystem compounds the reasoning model's capabilities with NVIDIA AlpaSim, an open simulation framework that enables scalable closed-loop testing, and the PhysicalAI-AV dataset offering over 1,700 hours of captured data. These AI models run directly on NVIDIA GPU-accelerated systems, requiring a minimum of one GPU with at least 24GB of VRAM to achieve optimal training and inference times.