Look Under the Hood of Self-Driving Development at GTC 2020


New conference sessions focus on infrastructure for autonomous vehicle training and validation.

by DANNY SHAPIRO

The progress of self-driving cars can be seen in test vehicles on the road. But the major mechanics for autonomous driving development are making tracks in the data center.

Training, testing and validating self-driving technology requires enormous amounts of data, which must be managed by a robust hardware and software infrastructure. Companies around the world are turning to high-performance, energy efficient GPU technology to build the AI infrastructure needed to put autonomous driving deep neural networks (DNNs) through their paces.

At next month’s GPU Technology Conference in San Jose, Calif., automakers, suppliers, startups and safety experts will discuss how they’re tackling the infrastructure component of autonomous vehicle development.

By attending sessions on topics such as DNN training, data creation. and validation in simulation, attendees can learn the end-to-end process of building a self-driving car in the data center.

Mastering Learning Curves

Without a human at the wheel, autonomous vehicles rely on a wide range of DNNs that perceive the surrounding environment. To recognize everything from pedestrians to street signs and traffic lights, these networks require exhaustive training on mountains of driving data.

Tesla has delivered nearly half a million vehicles with AI-assisted driving capabilities worldwide. They’re gathering data while continuously receiving the latest models through over-the-air updates.

At GTC, Tim Zaman, machine learning infrastructure engineering manager at Tesla, will share how the automaker built and maintains a low-maintenance, efficient and lightning-fast, yet user-friendly, machine-learning infrastructure that its engineers rely on to develop Tesla Autopilot.

As more test vehicles outfitted with sensors drive on public roads, the pool of training data can grow by terabytes. Ke Li, software engineer at Pony.ai, will talk about how the self-driving startup is building a GPU-centric infrastructure that can process the increasingly heavy sensor data more efficiently, scale with future advances in GPU compute power, and can integrate with other heterogeneous compute platforms.

For NVIDIA’s own autonomous vehicle development, we’ve built a scalable infrastructure to train self-driving DNNs. Clement Farabet, vice president of AI Infrastructure at NVIDIA, will discuss Project MagLev, an internal end-to-end AI platform for developing NVIDIA DRIVE software.

The session will cover how MagLev enables autonomous AI designers to iterate training of new DNN designs across thousands of GPU systems and validate the behavior of these designs over multi-petabyte-scale datasets.

Virtual Test Tracks

Before autonomous vehicles are widely deployed on public roads, they must be proven safe for all possible conditions the car could encounter — including rare and dangerous scenarios.

Simulation in the data center presents a powerful solution to what has otherwise been an insurmountable obstacle. By tapping into the virtual world, developers can safely and accurately test and validate autonomous driving hardware and software without leaving the office.

Zvi Greenstein, general manager at NVIDIA, will give an overview of the NVIDIA DRIVE Constellation VR simulation platform, a cloud-based solution that enables hardware-in-the-loop testing and large-scale deployment in data centers. The session will cover how NVIDIA DRIVE Constellation is used to validate safe autonomous driving and how companies can partner with NVIDIA and join the DRIVE Constellation ecosystem.

Having data as diverse and random as the real world is also a major challenge when it comes to validation. Nikita Jaipuria and Rohan Bhasin, research engineers at Ford, will discuss how to generate photorealistic synthetic data using generative adversarial networks (GANs). These simulated images can be used to represent a wide variety of situations for comprehensive autonomous vehicle testing.

Regulators and third-party safety agencies are also using simulation technology to evaluate self-driving cars. Stefan Merkl, mobility regional manager at TÜV SÜD America, Inc., will outline the agency’s universal framework to help navigate patchwork local regulations, providing a unified method for the assessment of automated vehicles.

In addition to these sessions, GTC attendees will hear the latest NVIDIA news and experience demos and hands-on training for a comprehensive view of the infrastructure needed to build the car of the future. Register before Feb. 13 to take advantage of early rates and receive 20% off with code CMAUTO.

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