by Junko Yoshida
The effort to design highly automated vehicles is a battle scene with a cast of thousands—ranging from chip suppliers to tier ones, OEMs, component suppliers and software developers.
Graphics chipmaker Nvidia has snapped up three new partnership deals, all of which leverage the company’s AI car-computing platform.
Last week, Nvidia announced that Volvo and Autoliv have selected its Drive PX 2 for production of self-driving cars in 2021. The company has also sealed a deal with ZF & Hella, who are both committed to working with Nvidia to deliver with the New Car Assessment Program (NCAP) safety certification for the mass deployment of self-driving vehicles.
But wait. There’s more. Nvidia also disclosed an agreement Volkswagen, under which the German carmaker will expand deep learning “competence” throughout the enterprise and developing a number of AI apps running in the data centre.
Prior to these disclosures, Nvidia had already picked up a number of other notable car OEMs and tier ones as partners for autonomous vehicle development. Among them, Tesla has been already using Drive PX in its current-generation cars. Audi is planning to deliver Level 4 cars based on Drive PX platform in 2020 and Toyota will use Nvidia’s platform to power advanced autonomous driving systems. Separately, Daimler, Mercedes Benz (owned by Daimler) and tier one Robert Bosch have also chosen Nvidia as their autonomous platform partner.
Figure 1: Nvidia’s relationship map. (Source: EE Times)
Nvidia’s senior automotive director Danny Shapiro told reporters, “The momentum of autonomous vehicles” is growing. The focus of activity is shifting from development to the “production phase.”
Noting 225 different “engagements” involving its Drive PX platform, Shapiro said that Nvidia is working with “a whole spectrum of players in the automotive industry ranging from OEMs, tier ones to trucks, HD mapping companies, sensors and start-ups.”
Citing Nvidia’s deal with Volkswagen, Shapiro said AI is now being applied not just to vehicles (i.e. path planning), but also to a backend system where a data centre crunches out traffic patterns, flows and driving behaviour while looking for anomalies—figuring out the entire transportation ecosystem.
Luca De Ambroggi, principal analyst for automotive electronics at IHS Markit, agreed. “This is the power of Nvidia offering a well distributed and consistent ‘solution’ in different domains from the ‘edge’ up to the infrastructure,” he said. “A lot of money (for the OEMs) stays in functionalities like predictive diagnostic and maintenance, cybersecurity and traffic management.”
Previously some sceptics noted that Nvidia’s AI platform might be effective for research, but not necessarily for production cars. However, Nvidia appears to be defying those predictions.
Ian Riches, director of global automotive practice at Strategy Analytics, told EE Times, “On the basis of public announcements, Nvidia would seem to be in the lead at the moment. That was my assessment before this latest round of news, so this has only reinforced that view.”
Phil Magney, founder and principal at Vision Systems Intelligence (VSI), agreed. “I can hardly think of a single OEM not working (or developing) on Nvidia DrivePX technology. This does not mean they will all go into production with Nvidia. It’s just that OEMs cannot afford to not examine the Nvidia eco-system for AI-based safety and automation technology.”
How fluid is the situation? Of course, none of the announced partnerships is exclusive. More important, the effort to design highly automated vehicles is a battle scene with a cast of thousands—ranging from chip suppliers to tier ones, OEMs, component suppliers and software developers.
A big question is the fluidity of these partnership arrangements. How easily might a car OEM committed to one platform (such as Drive PX) switch to another (such as the one by Intel/Mobileye/BMW)?
Strategy Analytics’ Riches said, “A commitment to a certain platform does not mean that it will never change—but does show a high degree of confidence that it is the best available solution for the short- and medium-term.”
He added, “There is always a cost to change. Software will be optimised for a particular hardware architecture, and engineers will get used to and skilled with a certain ecosystem of tools.”
When asked how hard to switch platforms, VSI’s Magney told EE Times, “There is no plug and play Automated Vehicle (AV) stack. Once an OEM has committed it is probably going to stick with it, at least for that generation of vehicle.”
VSI is currently engaged in developing an automated vehicle for its own research purposes. Magney said, “The development of AV functions is very difficult, as we are learning for ourselves. Stitching together all the code bases, synchronising sensors, calibrating torque signals, controlling latencies, etc., takes massive investments in engineering resources.”
He added, “Furthermore, developing and integrating the software stack into the hardware platform is also tough and very time consuming. Some of the AV Stacks come with an abstraction layer for adapting software applications a little bit easier, but in the case of AV Development platforms, there are still lots of gaps.”