The large dataset for teaching your algorithms to drive can be downloaded from http://bdd-data.berkeley.edu/.
It contains over 100,000 HD video sequences, that make up over a thousand hours of footage. The data contains over 100 000 annotated images for object detection for bus, traffic light, traffic sign, person, bike, truck, motor, car, train, and rider. Alos segmentation, drivable area, lane markings etc.
I love how data is released to the public for the greater good.
If you don’t have the time or money to spend on Udacitys Self Driving Car nanodegree, perhaps you want to try anyway to make a car drive by itself. Perhaps your real car is not ideal for training the algorithms, then you can use the provided simulator provided by Udacity that runs in Unity.
Check this blog pos tour for some tips: Training a deep learning model to steer a car in 99 lines of code
I found a great course for learning to create self driving cards using Deep learning, Deep Reinforcemet Learning, Convolutional Neural Networks and Recurrent Neural Network for different parts of the tasks needed to be solved in producing an autonomous vehicle that can adapt to traffic, control a car, learn to drive and steer through time.
Continue reading “Deep learning for Self driving cars”