There has been a lot of hype around autonomous vehicles over the past few years but the reality is that the technology still seems some time away from becoming mainstream.
For instance, Tesla CEO Elon Musk makes a prediction every year that his company's cars could be driving themselves soon without the help of any human, but he has been making that prediction for nine years now. Alphabet's self-driving division, Waymo, was founded back in 2009, but its cars continue to be plagued by glitches.
In other words, autonomous driving hasn't become foolproof yet. Of course, automakers and component suppliers have introduced various levels of autonomy in their products, but full autonomy hasn't become a reality. Still, companies involved in this space continue to test their technologies and pour money into autonomous driving systems.
That's not surprising as the global autonomous vehicle market is expected to generate a whopping $2.3 trillion in revenue by 2030 compared to $147 billion last year, according to Next Move Strategy Consulting. Semiconductor giant Nvidia (NVDA -3.33%) is going to be at the forefront of this massive growth as its artificial intelligence (AI) technology has been playing a critical role in helping autonomous vehicles get better. Let's look at the reasons why.
Nvidia's graphics cards have been playing a key role in autonomous driving
Nvidia has been involved in the development of self-driving cars for a long time. In January 2015, Nvidia announced the launch of the Drive CX and Drive PX automotive platforms. While Drive CX was a digital cockpit solution, Drive PX was an image-processing solution to aid the development of self-driving cars. The company upped its game in 2016 with Drive PX2, which it claimed was the world's first in-car AI supercomputer.
Drive PX2 used Nvidia's graphics processing units (GPUs) and deep learning algorithms to help the company's automotive partners develop self-driving solutions. The good part is that Nvidia has honed its automotive AI technology over the years with the help of more powerful GPUs. The company unveiled its latest generation of Nvidia Drive, known as Thor, in September last year.
According to Nvidia, Drive Thor "achieves up to 2,000 teraflops of performance [and] unifies intelligent functions -- including automated and assisted driving, parking, driver and occupant monitoring, digital instrument cluster, in-vehicle infotainment (IVI) and rear-seat entertainment -- into a single architecture for greater efficiency and lower overall system cost."
That's a big jump over the Drive PX2's computing performance of 8 teraflops, suggesting that Nvidia's autonomous vehicle platform evolved big-time over the years and is now more capable. That's not surprising as Nvidia's GPU architecture has improved significantly since the launch of the Drive PX, which was based on the 28-nanometer (nm) Maxwell architecture. Thor, meanwhile, is based on the 5 nm Ada Lovelace architecture, which explains the terrific bump in performance over Nvidia's first-gen Drive platform.
A smaller process node helped Nvidia pack a greater number of transistors more closely in its chips, allowing it to generate more computing power and reduce power consumption. As a result, Nvidia's GPUs are now way more powerful for tackling AI workloads and powering self-driving cars than they were a few years ago.
After all, GPUs are the backbone of autonomous driving functions such as advanced driver assistance systems (ADAS) thanks to their ability to carry out millions of calculations simultaneously. They can process huge amounts of data quickly, which is the key to making decisions in real time for self-driving cars. Nvidia executive Danny Shapiro believes that Thor will enable automakers to scale up to full autonomy, which would eliminate the need for human intervention in vehicles, indicating that the company is likely to be at the forefront of self-driving technology in the future.
More importantly, the company's position in the autonomous vehicle market could supercharge its growth in the long run.
Autonomous vehicles could become big business for the chipmaker
Nvidia's automotive segment is currently a small portion of its overall business, but it has been growing at a nice clip. The company generated $903 million in automotive revenue in fiscal 2023, a 60% jump over the prior year. For comparison, Nvidia's overall revenue stood stagnant at nearly $27 billion during the fiscal year.
Nvidia credited its robust automotive growth to the "growth in sales of self-driving solutions, computing solutions for electric vehicle makers and strength in sales of AI cockpit solutions." However, this is just the beginning as Nvidia's potential automotive pipeline stands at $14 billion for the next six years, indicating that this business is likely to gain more momentum.
Even better, Nvidia sees a $300 billion long-term opportunity in the automotive market, driven by the growing levels of automation and the increasing demand for image processing that its GPUs carry out. The good part is that Nvidia has built a solid ecosystem of automakers, component suppliers, and software vendors to tap this huge opportunity.
Mercedes-Benz, Volvo, BYD, Navistar, and Hyundai are some of the names that are utilizing Nvidia's self-driving AI technology. As these companies bring their products to the market, the tech giant should be able to convert more of its potential automotive opportunity into actual revenue and accelerate its growth in the long run.
