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. Autonomous vehicles appear more often on public roads, including in cities such as London and San Francisco. (CREDIT: Zoox) …