Korean AI Model Pre-Scores Every Driving Path for Safety
Source: The Next Web
Rather than mimicking human driving patterns, this approach generates and evaluates all possible trajectories before execution—a departure from the black-box learning that dominates autonomous vehicle development. Explainability matters because regulators, insurers, and courts will demand to know *why* a car chose a particular path in a collision scenario, and "the neural network decided" won't suffice. If this method scales beyond controlled CVPR demonstrations, safety-critical AI in industries facing similar liability pressures may need to adopt similar reasoning-based architectures.