Autonomous vehicles are now technically feasible, but their development is increasingly constrained by a fundamental scalability issue: the explosion of driving scenarios required to train and validate learning-based systems. Industry leaders such as Waymo report hundreds of millions of real-world kilometers driven, complemented by billions of simulated kilometers. Recent advances in large-scale simulation, notably announced by NVIDIA at CES 2026, further increase the volume of data that must be generated, processed, and validated, without solving the underlying problem, although it is a stunning new technology.

Driving occurs in an open world, where the number of possible scenarios is inherently unbounded. As a result, scenario-based approaches face growing complexity, cost, and validation challenges.
Arocern, addresses this limitation through a prudence-based paradigm. Instead of reasoning at the scenario level, we introduce a prudence metric that evaluates, at every instant, whether a vehicle - human-driven or autonomous - is behaving prudently with respect to uncertainty, visibility, distance, and reaction margins. Our key insight is that prudence functions act as the elementary building blocks of motion control, enabling complex driving behavior to be decomposed into a small set of fundamental interactions.

This approach enables a drastic reduction in required training and validation data, supports true unit testing of autonomous driving functions, significantly reduces hardware and simulation costs, and accelerates development cycles.

Arocern’s proprietary Anticipative Prudence technology represents a disruptive shift in autonomous driving: moving from enumerating scenarios to enforcing principled, scalable, and anticipatory safe behavior, providing a credible path to escaping the scenario explosion.