Autonomous vehicles have made remarkable progress over the past decade. Driverless cars and buses that once struggled to stay in lane can now navigate busy city streets, recognise pedestrians and cyclists, and respond smoothly to traffic signals. Yet one challenge remains stubbornly difficult. The hardest situations on the road are not the common ones but the rare and unpredictable events – what AI researchers call “long-tail scenarios” or “edge cases”, because they occur as outliers on any event distribution curve. Examples include unexpected roadworks, unusual behaviour from other road users, and other subtle situations where there is a very low probability of something happening – but which would have a significant impact on the vehicle and journey. Addressing these issues needs more than just better sensors – it requires vehicles that can reason about uncertainty. The most promising class of AIs yet developed to do this are known as “vision-language-action” (VLA) models. These take visual inputs from sensors, form an internal reasoning process often described as “thinking in steps”, then (almost instantaneously) generate actions such …