Zero2FSD is a free, self-study course from zero to research level in autonomous driving. From Week 1 you drive a complete but bad car in a simulator, and every week you replace one of its boxes with code you wrote. The course moves from the whole game to the classical car, the learned car and research. It runs on Colab, and your progress stays in this browser. Labs and source are in the course repo.
The course
Every week is a short lecture, a Colab lab where you write one box of the car, a quiz, and a teach-back.
Play a junior version of the whole game. You get a complete car that drives badly, then open its boxes one at a time and swap in your own.
Measure a lane, find paint in an image, estimate the lane, steer from the estimate, and watch the car fail on a tight bend.
Paper: ALVINN: An Autonomous Land Vehicle in a Neural Network (Pomerleau, NIPS 1988)
Frames and transforms — 5 weeks
Longitudinal control and vehicle motion — 6 weeks
Lateral control — 5 weeks
Paper: Stanley: The robot that won the DARPA Grand Challenge (Thrun et al., 2006)
Camera geometry — 6 weeks
Classical lane perception — 6 weeks
State estimation — 7 weeks
Paper: A New Approach to Linear Filtering and Prediction Problems (Kalman, 1960)
Multi-object tracking — 5 weeks
Planning — 6 weeks
Classical car capstone — 2 weeks
Autograd from scratch — 5 weeks
ML fundamentals — 5 weeks
Neural nets and CNNs in PyTorch — 5 weeks
Learned lane perception — 5 weeks
Object detection on real frames — 6 weeks
Multi-task nets — 3 weeks
Attention and transformers from scratch — 5 weeks
BEV perception and point clouds — 6 weeks
Occupancy and temporal fusion — 4 weeks
Motion forecasting — 4 weeks
Imitation-learned planner — 4 weeks
Reading and reproducing papers — 3 weeks
RL for driving in the course sim — 6 weeks
End-to-end driving — 5 weeks
World models and generative simulation — 5 weeks
Vision-language-action models — 4 weeks
Data engines and the long tail — 4 weeks
Safety, verification and scenario-based evaluation — 5 weeks
Thesis — 8 weeks