Part 4: With the (tedious) hardware and software setup out of the way, we will dive right into the FUN parts! Our first project is to use python and OpenCV to teach DeepPiCar to navigate autonomously on a winding single lane road by detecting lane lines and steer accordingly.

Step-by-Step Lane Detection

Part 5: we will train DeepPiCar to navigate the lane autonomously without having to explicitly write logic to control it, as was done in our first project. This is achieved by using “behavior cloning”, where we use just the videos of the road and the correct steering angles for each video frame to train DeepPiCar to drive itself. The implementation is inspired by NVIDIA’s DAVE-2 full-sized autonomous car, which uses a deep Convolutional Neural Network to detect road features and make the correct steering decisions.

Lane Following in Action

Lastly, in Part 6: We will use deep learning techniques such as single shot multi-box object detection and transfer learning to teach DeepPiCar to detect various (miniature) traffic signs and pedestrians on the road. And then we will teach it to stop at red lights and stop signs, go on green lights, stop to wait for a pedestrian to cross, and change its speed limit according to the posted speed signs, etc.