Advanced Driver Assistance Systems development and deployment requires the ability to acquire, store and manage massive amounts of data, high performance computing capacity and advanced deep learning frameworks, along with the capability to do real-time processing of local rules and events in the vehicle.
The amount of data generated by an autonomous car is so high that cloud computing is one of the enablement factors which make autonomous driving in its current fashion possible. Cloud solutions have unlimited storage and compute capacity and support for deep learning frameworks such as TensorFlow, PyTorch and MxNet.
Beyond accelerating algorithm training and testing, some ADAS implementations are even going as far as executing the neural network in the cloud. Such methods heavily rely on connectivity and bandwidth available between a moving vehicle at speed and cloud infrastructure. BitRoute service is used to intelligently solve the problem of bandwidth unpredictability and fluctuations.