  A convolutional neural network (CNN) approach is used to implement a level 2
autonomous vehicle by mapping pixels from the camera input to the steering
commands. The network automatically learns the maximum variable features from
the camera input, hence requires minimal human intervention. Given realistic
frames as input, the driving policy trained on the dataset by NVIDIA and
Udacity can adapt to real-world driving in a controlled environment. The CNN is
tested on the CARLA open-source driving simulator. Details of a beta-testing
platform are also presented, which consists of an ultrasonic sensor for
obstacle detection and an RGBD camera for real-time position monitoring at
10Hz. Arduino Mega and Raspberry Pi are used for motor control and processing
respectively to output the steering angle, which is converted to angular
velocity for steering.
