Researchers from MIT and Motional developed CW-Net, a method that helps people anticipate when a self-driving vehicle may make a mistake.

It translates decisions inside the vehicle’s AI planner into understandable concepts such as “close to cyclist” without changing driving performance. In private-track and simulation tests, the explanations helped participants predict vehicle behaviour more accurately.