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Autonomous driving is one of the most demanding problems in physical AI. An automated system must interpret a chaotic, ever-changing world in real time—navigating uncertainty, predicting human behavior, and operating safely across an immense range of environments and edge cases.
At General Motors, we approach this problem from a simple premise: while most moments on the road are predictable, the rare, ambiguous, and unexpected events — the long tail — are what ultimately defines whether an autonomous system is safe, reliable, and ready for deployment at scale. (Note: While here we discuss research and emerging technologies to solve the long tail required for full general autonomy, we also discuss our current approach or solving 99% of everyday autonomous driving in a deep dive on Compound AI.)
As GM advances toward eyes-off highway driving, and ultimately toward fully autonomous vehicles, solving the long tail becomes the central engineering challenge. It requires developing systems that can be counted on to behave sensibly in the most unexpected conditions.
GM is building scalable driving AI to meet that challenge — combining large-scale simulation, reinforcement learning, and foundation-model-based reasoning to train autonomous systems at a scale and speed that would be impossible in the real world alone.
Stress-testing for the long tail
Long-tail scenarios of autonomous driving come in a few varieties.
Some are notable for their rareness. There’s a mattress on the road. A fire hydrant bursts. A massive power outage in San Francisco that disabled traffic lights required driverless vehicles to navigate never-before experienced challenges. These rare system-level interactions, especially in dense urban environments, show how unexpected edge cases can cascade at scale.
But long-tail challenges don’t just come in the form of once-in-a-lifetime rarities. They also manifest as everyday scenarios that require characteristically human courtesy or common sense. How do you queue up for a spot without blocking traffic in a crowded parking lot? Or navigate a construction zone, guided by gesturing workers and ad-hoc signs? These are simple challenges for a human driver but require inventive engineering to handle flawlessly with a machine.
Autonomous driving scenario demand curve GM's rigorous solutions toolkit: VLA models, dual frequency VLA, simulations, seed-to-seed translations, GM gym & boxworld, on-policy distillation, SHIFT32, epistemic uncertainty head.
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