Over the last 10 years, a number of players have developed autonomous vehicle (AV) systems using deep neural networks (DNNs). These systems have evolved from simple rule-based systems to Advanced Driver Assistance Systems (ADAS) and fully autonomous vehicles. These systems require petabytes of data and thousands of compute units (vCPUs and GPUs) to train.
This post covers build approaches, different functional units of ADAS, design approaches to building a modular pipeline, and the challenges of building an ADAS system.
DNN training methods and design
AV systems are built with deep neural networks. When it comes to the design of an AV system, there are two main approaches. The difference is based on how the DNNs are trained and the system boundary.

Modular training – With a modular pipeline design, the system is split into individual functional units (for example, perception, localization, prediction, and planning). This is a common

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