NIVIDA is the leader in Deep Learning and GPU. For the past few years, it is gaining in market momentum and stock value (Investments from Sunsoft is another proof). Its Deep Learning technology not only drives cars; it helps self-driving cars to improve skills overtime. RTC Magazine’s Editor-in-Chief, John Koon caught up with Danny Shapiro, Senior Director of Automotive to gain his latest insight is future cars.

by John Koon, Editor-in-Chief

1. What is your vision of future self-driving cars? How important is machine vision in making self-driving cars commercially feasible?

Self-driving cars will have an incredibly positive effect on society access to transportation will be transformed. Autonomous cars will not only redefine the way people commute, giving them hours back each day, but will change how goods are transported.

We believe machine vision plays a role to make cars commercially feasible, but it is not the sole answer to an autonomous future. Just like we use our five senses to navigate the world around us, we believe for cars to better pilot themselves, they should also include other sensors such as radar, lidar, ultra-sonic, and HD maps to further augment the execution of an aware SDC.

Fundamentally AI is essential to be able to take data coming from these sensors, and be able to interpret it. There is no way that computer vision algorithms can be programmed to account for the near infinite number of scenarios that happen on our roads. But with deep learning, autonomous vehicles can be trained to drive better than humans.

2. What hurdles need to be overcome before fully autonomous vehicles can be achieved? Do you think the 2020 goals are achievable?

Currently the biggest hurdle autonomous technology companies are facing is the legislative red tape. State and federal regulators are having a hard time keeping up with the cadence of these new technologies. However, just in the last year alone, there have been leaps and bounds in improvements in coming up with a streamlined plan for the rollout of self-driving cars. NVIDIA recently testified in front of the U.S. Senate Committee on Commerce, Science, and Transportation for the need to implement AI in self-driving cars, and provided guidance on rule making to ensure safe deployment of this vital technology on our roads.

Is 2020 achievable? Yes absolutely. NVIDIA is developing systems to bring fully autonomous cars by 2020 that will be able to operate in specific environments. OEMs such as Audi announced this year that by 2020 they will have level 4 capable vehicles powered by NVIDIA ready for market deployment.

3. In your opinion, what technologies will be used in self-driving vehicles? Examples include: radar, machine vision, deep learning/artificial intelligence, smart sensor, IoT and big data analytics. How does vehicle-to-vehicle technology fit in? What is missing?

Everything you mentioned will all play a vital role in the rollout of autonomous vehicles. But we believe what plays one of the biggest roles is deep learning. Through deep learning, the entire suite of sensors will be able to have a much greater understanding of what is happening at any given moment. Deep learning also plays a major role in big data analytics. Information these vehicles are generating along with smart city information can be used improve traffic flow.

V2V technology is a nice to have capability in a car, but it is not essential. A vehicle must be able to navigate autonomously even before V2V communication is established. Similarly, connectivity to the cloud cannot be a requirement for self-driving. All processing for autonomy must take place on board the vehicle – hence the need for an energy efficient supercomputer, design for sensor fusion and deep learning.

4. How important is the infrastructure such as smart freeway to the success self-driving cars?

Vehicle-to-infrastructure (V2I) will further augment the driving experience, however given there are no standard implementations, or widespread adoption; this is not a useful solution in the short or even medium term. Self-driving cars need to be self-contained. With a programmable and updateable platform on board, software updates can leverage V2I and V2V data when it is available.

5. What contribution does your company make to the field of self-driving cars?

While sensors play a vital role in the operation of autonomous cars a powerful computing platform needs to be able to make sense of the information these sensors are generating. The NVIDIA DRIVE PX car computing platform is designed to handle the entire driving pipeline including sensing, localization and path planning. The platform is designed for deep learning inferencing and is capable of performing 30 trillion operations per second while only consuming 30 watts. In addition, NVIDIA also developed a complete, open software development stack for companies to use when developing their autonomous cars, shuttles, large trucks, and more. Currently over 225 OEMs, Tier 1s, start-ups, HD mapping companies, and research institutions are currently using our solutions for an autonomous future.

Santa Clara, CA
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Danny Shapiro is Senior Director of Automotive at NVIDIA, focusing on artificial intelligence (AI) solutions self-driving cars, trucks and shuttles. The NVIDIA automotive team is engaged with over 225 car and truck makers, tier 1 suppliers, HD mapping companies, sensor companies and startup companies that are all using the company’s DRIVE PX hardware and software platform for autonomous vehicle development and deployment. Danny serves on the advisory boards of the Los Angeles Auto Show, the Connected Car Council and the NVIDIA Foundation, which focuses on computational solutions for cancer research. He holds a Bachelor of Science in electrical engineering and computer science from Princeton University and an MBA from the Haas School of Business at UC Berkeley.