How to Benefit from AI in the Automotive & Transportation Industry
If you work in the automotive and transportation industry, spend a few minutes to read our in-depth analysis of how artificial intelligence has created new opportunities in this sector, and what tools you can use to get ahead. This article is part of GIGABYTE Technology’s ongoing “Power of AI” series, which examines the latest AI-related trends, and how intrepid visionaries can reap the benefits of this exciting paradigm shift.
Artificial intelligence (AI) is the catalyst of a profound change in the automotive and transportation industry. The injection of cutting-edge computing power in every device that’s involved in the process of moving passengers from point A to point B means that our vehicles and roads can help to advance the objectives of convenience and safety—without relying on human input. This exhilarating paradigm shift can be most directly observed through the proliferation of autonomous vehicles, also called driverless or self-driving cars, and intelligent transportation systems (ITS), also known as “smart traffic”.
As it pertains to automotive and transportation, AI is an umbrella term that covers a vast selection of technological milestones, all of which revolve around helping the machines “perceive” the world around them, “process” the significance of the data, and “produce” actionable results. This is why you’ll hear terms like “computer vision” or “deep learning” being invoked in discussions about AI. High-performance computing (HPC) and edge computing play a major role in the development and deployment of these solutions.
As we move closer to the era when AI will be ready to take the wheel, it is crucial to note that this is a rapidly evolving market, where daring new breakthroughs can reshape the landscape overnight. In the following sections, we will explore the two exemplary AI innovations of this sector—autonomous vehicles and ITS—as well as the tools you can use, and the success stories that you may refer to as you look for your own opportunities.
Autonomous Vehicles Training: Teaching the AI to drive
According to SAE International, there are 6 levels of driving automation, with Level 0 being no automation and Level 5 being full automation. Levels 1 to 4 stand for driver assistance, partial automation, conditional automation, and high automation, respectively. Developers advance the automation of self-driving cars by “training” the AI model with labeled data collected from various types of sensors, including camera, radar, and lidar. The AI is eventually able to recognize all the objects that it may encounter on the road, from pedestrians to traffic signs to other cars, even when it is exposed to unlabeled data in a real-life situation.
Because the AI must “study” a massive amount of data, and because much of the data is in graphical form, servers outfitted with advanced graphics processing units (GPUs) capable of parallel computing are essential in the training process. GIGABYTE Technology’s G-Series GPU Servers specialize in GPU acceleration. The G593-SD0 and G593-ZD2, which integrate NVIDIA’s HGX™ H100 8-GPU computing module with 4th Gen Intel® Xeon® Scalable or AMD EPYC™ 9004 CPUs, respectively, are some of the most powerful AI supercomputing platforms on the market.
A world-renowned Israeli developer of autonomous driving technology uses GIGABYTE’s GPU Servers to train its fleet of driverless cars. Every time a prototype vehicle goes out for a spin, it returns with 64 terabytes of raw data. The GIGABYTE servers are able to process the data with maximum efficiency.
Autonomous Vehicles Inference: Road tests and deployment
The process by which an AI model draws on its training to respond to new input is known as inference. That’s what the driverless car is doing when it navigates the road in real life: it is taking the information gathered by its array of sensors and entering it into the pre-trained model to generate instructions for its control system. Data from the inference phase may also be used in the next round of training to continuously improve the AI.
Computing resources may be remote or onboard during inferencing. GIGABYTE’s E-Series Edge Servers are highly recommended for edge computing scenarios that require computation to happen as physically close to the source of the data as possible to reduce latency and bandwidth use. Highly advanced processors can be effortlessly deployed in the ultra-dense and compact servers thanks to GIGABYTE’s proprietary thermal management and chassis design. With regard to embedded in-vehicle computing, GIGABYTE offers the Automated-Driving Control Unit (ADCU) to serve as the AI mobile edge computing platform at the heart of the vehicle, as well as the ARM-based In-Vehicle Telematics Control Unit (TCU) to provide real-time communication and fleet management capabilities.
Self-driving buses have hit the road in Taiwan’s Changhua Coastal Industrial Park. The electric shuttle bus “WinBus” can operate without human input as long as it follows a fixed route. GIGABYTE’s ADCU is employed to connect the vehicle’s sensors, batteries, and control system together to achieve Level 4 “high automation”.
Testing: Simulations to ensure safety
Extensive testing must be conducted before self-driving vehicles are ready to hit the road. One way to go about it is through computer simulations. Highly precise models in the vein of “digital twins” can recreate road conditions and traffic flow for the purposes of testing. For the model to be accurate, a number of data parameters must be collected through roadside sensors installed in our cities and on our highways. The key parameters include the number of vehicles, the distance between the vehicles, and vehicular speed.
Like a majority of cloud computing devices, roadside sensors use computer chips that follow the RISC architecture, which has the advantage of consuming less power. To develop traffic flow models more efficiently, it is beneficial to work with servers that “speak” the same computing language. Enter GIGABYTE’s ARM Servers, which are built around “cloud-native”, RISC-based ARM processors. Not only does this preclude the use of a compiler to “translate” between machines, but the nature of RISC also means that each individual processor can provide more cores, upping performance while lowering the TCO.
Taiwan University (NTU) is using GIGABYTE’s G242-P32 to incubate a “high-precision traffic flow model”. This is a highly synchronized, exceedingly accurate reproduction of road conditions in Taiwan, where synchronization between real conditions and digital representation must be within 0.05 seconds to ensure model accuracy. Development time is reduced by half due to the ARM-based CPUs. Scientists on the team laud the server as an all-in-one solution that can train the AI, develop the computer model, transfer the data, and more.
One of the standard-bearers for artificial intelligence in the automotive and transportation sector is the autonomous vehicle. Since a massive amount of data must be processed to “train” the AI driver, computing platforms that provide GPU acceleration are the go-to solution. GIGABYTE offers a wide range of G-Series GPU Servers that specialize in combining the prowess of advanced CPUs and GPUs to deliver a supercomputing platform right into your hands. A good example is the G593-SD0, which features 4th Gen Intel® Xeon® Scalable CPUs and NVIDIA’s HGX™ H100 8-GPU computing module.
Once the AI model has been trained, the self-driving car “inferences” by choosing the right responses to new data in the world, thus ensuring a safe and convenient journey. If inferencing is done through an onboard computer, GIGABYTE’s Automated-Driving Control Unit (ADCU) may serve as the brain of the driverless vehicle. If remote resources are needed to provide support, GIGABYTE’s E-Series Edge Servers can be deployed in locations outside of data centers to broaden coverage while reducing latency and bandwidth use.
In addition to training and inference, autonomous vehicles may be tested through computer simulations to guarantee road-readiness. A “high-precision traffic flow model” is one way of recreating road conditions in the lab. Since such models rely heavily on data collected via roadside sensors, a server that follows the same instruction set architecture as those devices can improve efficiency. GIGABYTE’s “cloud-native” ARM Servers are based on the RISC architecture and have been shown to cut development time in half in case studies.
Intelligent Transportation System
Governance: Building an AI-based ecosystem
The AI wave is not only revolutionizing the auto sector, it also has broad implications for our transportation ecosystem. After all, it stands to reason that an artificial intelligence smart enough to drive cars can also read license plates and manage toll stations. Going forward, it’s inevitable that AI will handle more and more governance-related tasks.
Utilizing computer vision to recognize the numbers and letters on license plates, which is applicable to both parking lots and highway tollbooths, is a prime example of AI inference. The amount of input is enormous—to use Taiwan’s Electronic Toll Collection (ETC) System as an example, 16 million new data points are processed every day. A powerful AI inference server, such as GIGABYTE’s G293-Z43, is perfect for the job. Its industry-leading ultra-dense configuration of 16 AMD Alveo™ V70 cards in a 2U chassis provides maximum acceleration with minimal physical footprint and latency.
As mentioned before, feedback from the inference phase can be used to improve the accuracy of an AI model’s output. The practice of establishing a pipeline to continuously update and revamp the AI is known as MLOps. GIGABYTE’s investee company MyelinTek Inc. offers the DNN Training Appliance, which is a software and hardware package for MLOps that can streamline the AI development process in transportation and many other fields.
Intelligent Transportation System
Communication: Constructing a network of IIoT/V2X devices
Another important role for AI to play in the transportation sector is empowering IIoT and V2X devices that will help to complete the smart traffic network. Diverse elements of our cities and boulevards, such as utility poles, traffic signals, and bus stops, can all become part of the ever-expanding AI infrastructure.
A versatile edge computing platform or a powerful embedded system is the best way to deploy artificial intelligence in this scenario. GIGABYTE’s AI Mobile Edge Computing Platform is the recommended solution for a majority of roadside installations. Its easy expandability allows for the addition of more accelerators, while the rich offering of I/O ports means that it can be adapted for use in just about any environment. The AI can provide the latest traffic updates to drivers and pedestrians alike, and it can also report to a control center so that administrators may make adjustments on a macro level.
GIGABYTE also offers industrial motherboards and embedded IoT solutions for clients in this sector. Notable features of these products include a wide range of operating temperatures, low power consumption, and superb connectivity. Processing speed doesn’t take a back seat, either, because GIGABYTE’s solution with Blaize provides inference acceleration on the edge that allows artificial intelligence to proliferate across the automotive and transportation industry.
Besides autonomous vehicles, AI can also be infused in traffic-related infrastructure to build an intelligent transportation system that governs the way we get about and offers support to make sure we reach our destinations safely and comfortably.
License plate recognition plays a major role in an AI-based transportation ecosystem. Computer vision is used to identify vehicles on highways and in parking lots. Since this is an inference-intensive task, a powerful AI inference server, such as GIGABYTE’s G293-Z43, can make a world of difference. The practice of using data from the inference phase to retrain the AI and update the model can be streamlined with GIGABYTE’s DNN Training Appliance, which is a total solution for MLOps that’s highly recommended for AI development in the automotive and transportation industry.
The realization of IIoT and V2X networks can help vehicles stay connected to their surroundings at all times, vastly improving safety and convenience. Different types of roadside installations, from streetlights to digital signage, can be the platforms for edge computing systems that may provide services such as traffic updates and ride-hailing. GIGABYTE’s AI Mobile Edge Computing Platform, industrial motherboards, and embedded IoT solutions can all be used to increase the saturation of artificial intelligence applications, which will provide useful support to every traveler on the road.
If you wish to be part of the AI revolution that’s sweeping through the automotive and transportation sector, you need a solutions provider that can put the most advanced AI inventions at your disposal, while also sharing knowledge about how the new methods can pave your way to boundless opportunities. GIGABYTE Technology, a leading supplier of computer hardware and a renowned pioneer in the tech industry, is the partner you need on your AI journey. We offer these attributes that will help you emerge victorious in the paradigm shift brought on by artificial intelligence.
● Comprehensive product line
As we demonstrated in the previous sections, GIGABYTE provides computing solutions for different stages of the development of autonomous vehicles and intelligent transportation systems: including AI servers for training, inferencing, and testing, and edge computing platforms for deploying the smart traffic network. Working with a provider that caters to so many aspects of the industry can streamline the process for customers. It’s also testimony to the extent of GIGABYTE’s expertise in this vertical market.
● State-of-the-art computing prowess
GIGABYTE works closely with prominent chip vendors in the field of supercomputing—including AMD and Intel for x86 CPUs, Ampere for ARM CPUs, and AMD, NVIDIA, and others for GPUs—to ensure that our computing platforms can leverage the most advanced processing power. The extensive and versatile range of suppliers guarantees that our clients can select the processing units most suited to their individual needs from a broad selection of optimal choices.
● Unique and proprietary product design
GIGABYTE’s proprietary chassis design and thermal management features give you the best compute density and a minimal footprint, which is essential in edge computing applications. As an additional bonus, the improved power efficiency of our solutions is highly recommended for long-term deployment, while also giving you a leg up in sustainability.
● Complementary value-added services
Others might charge you for server management software, but not GIGABYTE. All GIGABYTE servers come with GIGABYTE Management Console for no additional cost. This user-friendly tool empowers IT administrators to optimize and control servers remotely through a web-based browser, maximizing performance and ensuring high availability. GIGABYTE Server Management (GSM), a proprietary multiple server remote management software platform, is also available as a free download from the official GIGABYTE website.
● Proven track record spanning decades
Since its founding in 1986, GIGABYTE has dedicated its efforts to bettering the world with advanced technology. Its involvement in the server business began as far back as 2000; in 2023, it spun off the server business unit to establish a wholly-owned subsidiary named Giga Computing Technology. Decades of investment in the ecosystem means that GIGABYTE servers are seamlessly compatible with the latest AI solutions, touting certifications from industry leaders such as NVIDIA, Linux, Red Hat, and more.
● Service and support you can count on
GIGABYTE offers reliable service and support for its extensive range of products. An online eSupport system can be used to get in touch with professional service teams, and an FAQ can help you troubleshoot on the spot.
We stand at the crossroads in an ongoing journey of artificial intelligence-driven innovations that will redefine the way we work, rest, and get around. In the arena of autonomous vehicles and intelligent transportation systems, AI is already bringing our visions of the future into reality, beginning with the driverless cars that we ride around in and the smart traffic network that supports them.
At its core, a self-driving car is developed through the dual steps of AI training and inferencing; an additional step of testing with computer models can be undertaken to further enhance safety. Drawing from its decades of experience in the server sector, GIGABYTE Technology provides certified AI platforms that are powered by the latest processors and accelerators from chip suppliers such as AMD, Ampere, Intel, and NVIDIA. These chips are integrated with GIGABYTE’s proprietary server design to give our customers the best tools for developing AI for vehicles.
AI can also shoulder the responsibilities of governance and communication on our roads. License plate recognition is made possible with computer vision, a form of advanced AI. Roadside installations can interconnect to build a network of IIoT and V2X devices that communicate the latest information to travelers. In addition to its AI servers, GIGABYTE also offers edge computing platforms and embedded computers (IPCs) that can endow all parts of the infrastructure with AI.
The power of AI is moving ahead at full speed in the automotive and transportation industry. If you are looking for an IT solutions partner to help you capitalize on this exciting trend, we highly recommend that you reach out to GIGABYTE Technology and learn what we can do for you.
Thank you for reading “How to Benefit from AI In the Automotive & Transportation Industry”, a part of the “Power of AI” series by GIGABYTE Technology. We hope this article has been helpful and informative. For further consultation on how you can benefit from AI in your business strategy, academic research, or public policy, we welcome you to reach out to our sales representatives at email@example.com