For math/stats&probability in the specific problem domain of autonomous vehicles (of which self-driving cars are a subset) I'd recommend no other than Sebastian Thrun's Udacity course "Artificial Intelligence for Robotics" [https://www.udacity.com/course/artificial-intelligence-for-r...]. The content is practical, and Thrun, in my opinion, is great at breaking down complicated topics (e.g. Kalman filters) into simpler sub-components and explaining the intuition behind them. You'll get a heavy dose of math/stats&probability in the context of autonomous robots (and in certain cases, Thrun contextualizes to his work in the DARPA grand challenges while at Stanford, which laid the groundwork for his work at Google on the self driving car project [now Waymo]). I can't speak to how much the "traditional" localization, mapping, and planning techniques taught in the course crossover to deep learning approaches, but you'll no doubt get the math/stats&probability knowledge you're looking for, in the context of self-driving cars.