Why do self-driving cars crash? King’s College London researchers think they have the answer New King’s College London algorithms aim to explain specific self-driving car crashes, not just predict future risk. Edited By: Joseph Shavit A self-driving car can make a mistake in seconds, but the reason it happened may stretch far back through a long chain of decisions. That is part of what makes autonomous vehicle crashes so hard to explain, and so hard to prevent. A team at King’s College London says it has developed a new way to tackle that problem. Instead of only estimating how likely a failure is to happen again, the approach is designed to work backward through a crash and identify why a specific failure occurred. That distinction matters as autonomous vehicles appear more often on public roads, including in cities such as London and San Francisco. Collisions and serious road safety breaches have sharpened pressure on manufacturers to explain what went wrong when these systems fail. Current methods can offer only limited answers. They tend to rely on failure statistics, which are useful for measuring risk but weaker at explaining one concrete event. “Traditional methods rely on compiling failure statistics, to tell us how likely another failure is to happen in the future, but they cannot definitively tell you why a self-driving car made the specific error it did. For that, you need to leverage what is known as ‘actual causality’, where an algorithm analyses past mistakes retrospectively,” said Dr Khen Elimelech, leader of the Autonomous Robots Lab at King’s and first author of the paper. Looking backward after the crash The research centers on a concept known as actual causality. In simple terms, that means examining events after a failure has happened and asking which of them truly caused the outcome.