Kodiak, an autonomous truck maker, is using probabilistic risk assessment and an AI tool called BreakPoint to measure and prove the safety of its self-driving system in real-world scenarios.

What happened: Kodiak has adopted two safety tools—Probabilistic Risk Assessment (PRA), a methodology that estimates collision rates and identifies key risk factors, and BreakPoint, an internally developed AI validation tool that hunts for edge cases that could cause collisions or undesirable behavior. Together, these tools form the core of Kodiak's approach to safely developing and deploying its AI-powered autonomous driver.

Why it matters: Autonomous vehicle safety cannot be claimed—it must be proven with hard numbers, not gut feelings. The PRA method, which borrows techniques from aerospace and nuclear energy, uses Bayesian probability theory and statistical models to calculate expected collision rates for scenarios so rare they may not occur in real-world testing alone. This allows Kodiak to demonstrate safety rigorously and compare its performance against human driving baselines established with leading transportation research centers.

What to watch: The PRA is designed as an iterative, living process rather than a one-time compliance check. As Kodiak collects more data, the PRA updates to reflect increased knowledge and characterizes the uncertainty in the safety assessment itself—making clear where evidence is strong and where it still needs to grow.