Tutorials

These tutorials provide an introduction to using the Autonomous Driving Platform. Work through them in order to bring your hardware online, collaborate effectively, and iterate on increasingly advanced autonomy stacks.

• Intro to Car Hardware — Bring the platform online, connect over WireGuard, and stream core sensors through Foxglove.
• Building Robots in Teams — Collaborate with your team to learn the basics of ROS2 and create a shared GitHub project.
• Camera Calibration and LiDAR Projection — Calibrate the camera, undistort imagery, and overlay LiDAR points for multi-sensor perception.
• IMU Kalman Filter — Clean IMU data and fuse it with range readings to estimate wall distance robustly.
• Localization and Obstacle Avoidance via AMCL — Combine a reactive avoidance loop with AMCL to localize and navigate safely.
• Rule-Based Lane Following — Convert lane detections into steering commands using a hand-engineered policy.
• Data Collection for Imitation Learning — Capture synchronized sensor and control logs while driving expert laps for imitation learning.
• Train and Deploy an MLP Steering Policy — Train a neural network on collected data and deploy it for onboard autonomous driving.
• Reinforcement Learning for Lane Following — Build a simulation loop and train an RL agent to stay centered in the lane.