GoKawiilConcept-based explanations help humans build better mental models of self-driving car AI
Researchers built CW-Net, an explainable version of a black-box machine learning planner (based on the DriveIRL architecture) used to evaluate self-driving vehicle trajectories. The system was trained on two large datasets of millions of driving scenarios labeled with human-understandable concepts, allowing it to justify its trajectory choices in terms people can interpret rather than as opaque scores.