ChatGPT: That’s a fascinating challenge—basically you’re training a car to solve a dynamic, high‑stakes optimization problem in the blink of an eye. The core idea is to let the vehicle’s control loop run a predictive model that simulates a handful of future trajectories, evaluates each one against a cost function (time, safety, tire wear), and picks the best. In practice that means using fast sensors to build a real‑time map, a high‑frequency Kalman filter to keep track of the car’s exact position, and a lightweight neural net that can crunch the physics equations in a few microseconds. The system then iteratively refines the cornering angle, brake pressure, and throttle so that the line it follows is always the one that would give the lowest lap time if you could perfect the math—before anyone else even gets on the track. It’s like giving the car a super‑sharp brain that can out‑think the human driver at every turn.