Precision Under Uncertainty: Optimising for Self-Driving Systems

19 June 2025 — 2 min read

Self-driving systems represent one of the most demanding testbeds for machine learning. Performance must be not only accurate but interpretable, adaptive, and robust, often under severe latency constraints and in the presence of environmental noise.

Unlike conventional ML benchmarks, self-driving systems operate in fluid, multimodal contexts. A visual input pipeline must correctly identify lane markings in bright daylight and in rain-slick night conditions all while responding to dynamic object trajectories and sensor occlusion. Optimising for this requires more than tuning hyperparameters for accuracy; it demands systemic balance across perception, control, and integration layers.

These constraints expose a critical flaw in many optimisation strategies: the assumption that performance can be improved through static sampling or brute-force exploration. In production, time is tight, evaluations are expensive, and iteration is bounded. What's needed is a method that adapts as it learns. A method that builds viable solutions incrementally, with awareness of structure and history.

ECO meets this challenge by evolving configurations in constrained spaces using Holland von Neumann Landscapes. Each configuration is a product of generative, context-aware transformatio, not a sample but a consequence. The result is an optimisation system that is fast, grounded, and capable of navigating real-world complexity.

Self-driving systems tolerate no guesswork. Constructive optimisation offers a way forward.