The Economist AI Insight

Wayve AV Risk & Autonomy Simulator

Live Perception & Spatial Navigation
Alex Kendall / Wayve Core Thesis: Traditional HD-map autonomous systems struggle with novel UK road features (narrow unmapped lanes, jaywalkers, rain blur) because rule-based hand coding cannot cover millions of long-tail edge cases. Embodied AI uses end-to-end computer vision and learned physics models to generalise safely across unmapped environments.
Risk Metrics & Safety Benchmark
Disengagements / 100mi 0.12 Target: < 0.05
Near-Miss Events 1 Edge Case Trajectories
Safety Score 94.2 100 = Parity Target
Human Redundancy 7.5 yrs Projected Timeline
Human Driver Redundancy Timeline Curve Year 2026 - 2038
AV Architecture: Embodied AI (Wayve)
Disengagement Rate: 0.12 / 100mi
Safety Parity Score: 94.20 / 100
Redundancy Horizon: 7.5 Years
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