Machine learning for autonomous driving

Firstly, in order to develop an end-to-end self-driving car we must know the development process at a high-level before delving into the use of reinforcement learning in the whole process. The following is a diagram that depicts the development process:

As shown in the preceding figure, the first step of the process is the collection of sensor data. Sensors comprise a camera, LIDAR, IMU, RADAR, GPS, CAN, and many more devices that can capture the state of the vehicle as well as the surrounding environment in the best possible way. After receiving these sensory signals, they are preprocessed, aggregated, and then prepared for sending to the next process, which includes machine learning (ML) and analysis in the data center. This step of implementing ML on the prepared sensory signals is a key part, which involves state estimation from the input data, thereby modeling it, predicting the possible future actions, and finally, the planning as per the predicted...