This article was updated on June 7, 2017, and originally published on Jan. 9, 2015.

During its presentation at the 2015 Consumer Electronics Show, NVIDIA (NASDAQ:NVDA) spent nearly 90 minutes talking about cars. The company quickly introduced its latest mobile processor, the Tegra X1, and it showed a demo of the Unreal game engine running on it, but that was the last we'd hear about anything other than the automobile market.

That presentation marked the beginning of NVIDIA's aggressive efforts to position itself at the center of the self-driving car of the future. NVIDIA's Drive PX platform, now in its second iteration and powered by the company's Tegra mobile processors and discrete GPUs, is gaining traction among automakers. Drive CX, a platform aimed at powering in-car screens for navigation, digital instruments, and infotainment, is also part of NVIDIA's automotive strategy.

NVIDIA's Drive CX platform displaying virtual gauges.

Image source: NVIDIA.

The more exciting of the two platforms is Drive PX. Drive PX can identify objects around the vehicle based on inputs from multiple cameras and sensors. Having detailed information about its environment will be critical for the self-driving car of the future, and NVIDIA wants its chips to be the brains behind the operation. If NVIDIA can get out ahead of the competition and push its product as the standard technology, it could have a multibillion-dollar opportunity in the automotive market.

How Drive PX works

There are two main ways for a computer to detect objects from visual data. The first is for engineers to code specific feature detectors into the software. There would be one for stop signs, one for speed limit signs, and so on. Not only does this approach not scale very well given the enormous number of potential objects the system would need to detect, but variations, such as stop signs partially hidden by trees, or pedestrians holding objects, could prove problematic.

The second way is through deep learning, essentially training the system to detect features by feeding it an enormous amount of data. A supercomputer can be fed images tagged with what the images represent, and after churning through the data, it generates software that can then classify objects on the fly, in this case running on the Drive PX platform. When Drive PX can't identify an object, it has the capability to send the data to a data center so the system can be retrained, deploying a software update to all the cars in the network. In this way, the Drive PX system will become more accurate over time.

NVIDIA's Drive PX platform identifying objects.

Image source: NVIDIA.

To see an example of deep learning in action, the University of California, Berkeley, has a demo of its Caffe deep learning system, which runs on NVIDIA GPUs, at this website. Feed in any image, and the system will usually give a fairly accurate classification.

An enormous opportunity

NVIDIA's Drive PX platform now has over 200 partners, and every new Tesla Motors vehicle that rolls off the assembly line includes NVIDIA's hardware. Toyota has also jumped on the bandwagon, choosing Drive PX to power its future autonomous vehicles. Other major partners include Volvo, Bosch, and Audi

The big opportunity for NVIDIA is not only in increasing the number of cars containing its processors, but also boosting the number of processors per car. Fellow Fool Ashraf Eassa estimated in October 2014 that NVIDIA gets about $50 in revenue per car using its processor, but that number only included chips used to power displays. A future car containing a Tegra chip to drive the displays, along with additional chips for driver-assistance and self-driving features, could raise this revenue per car substantially.

About 88.1 million automobiles were sold in 2016 around the world, and the trend in the automotive industry is clearly toward smart, connected cars. Plenty of other companies are vying for a piece of the automotive pie, including Intel and Qualcomm. While the graphics produced by NVIDIA's Drive CX platform are impressive, the battle for the center console will be intense.

However, NVIDIA is right to focus on the driver-assistance and autonomous driving features that could be made possible by the Drive PX platform. This is, I think, the company's biggest opportunity in the automotive market, and it's an area where NVIDIA has a distinct advantage. Dominance in this market isn't going to be about simply supplying chips, but about providing entire solutions, software and all.

NVIDIA has done exactly that in other markets. For example, NVIDIA sells its line of Tesla GPUs aimed at supercomputing, high-performance computing, and other operations. But it also provides myriad tools and code libraries, and it ensures a vast catalog of applications, from AutoCAD to computational finance simulations, are ported to its proprietary GPU compute language. The ecosystem around the hardware is what ultimately drives sales.

The main challenge in bringing a self-driving car to market is software, not hardware. You can stuff a tremendous amount of computing power into a car, but it's the software that makes the car smart, and it's the software that will determine which company dominates the market. I suspect we're still many years away from a mass-market self-driving car, but when that day comes, NVIDIA is pushing hard for Tegra to be behind the wheel.