AI gives precision to autonomous vehicles in adverse weather

The breakthrough by researchers at Oxford University’s Department of Computer Science, in collaboration with colleagues from Bogazici University, Turkey, is detailed in a paper published in Nature Machine Intelligence.

In a statement, Yasin Almalioglu, who completed the research as part of his DPhil in the Department of Computer Science, said: “The difficulty for AVs to achieve precise positioning during challenging adverse weather is a major reason why these have been limited to relatively small-scale trials up to now.

“For instance, weather such as rain, fog, or snow may cause an AV to detect itself in the wrong lane before a turn, or to stop too late at an intersection because of imprecise positioning.”

To overcome this problem, Almalioglu and his colleagues developed a novel, self-supervised deep learning model for ego-motion estimation, which is a crucial component of an AV’s driving system that estimates the car's moving position relative to objects observed from the car itself.