Torc’s First Responder Program Recent Highlights

by Torc | Jun 26, 2025 | Education

Torc’s First Responder Team recently conducted comprehensive training sessions for more than 150 first responders representing 33 distinct agencies along the Texas I-35 corridor. This initiative is a critical part of Torc’s commitment to preparing the first responder community for interacting with Torc’s self-driving trucks on public roads.

Richard Russell, Senior Manager of First Responder Policy, and Foster Murphy, Fleet Compliance Specialist — with a combined 40+ years of law enforcement experience across city, county, and state levels – delivered training on Torc’s trucks to first responders from various Texas agencies, including Fort Worth PD, Waco PD, Williamson County Sheriff’s Office, and Johnson County Sheriff’s Office.

The sessions provided a deep dive into the essentials of autonomy and offered a hands-on experience with the Torc autonomous truck. Over two hours, Richard and Foster guided first responders through the operational mechanics of autonomous commercial motor vehicles, emergency response protocols, and safety procedures to ensure first responders are well-equipped to handle various scenarios involving Torc trucks.

Looking Ahead

Torc plans to expand its First Responder Training to additional agencies, focusing on where Torc trucks will be operating along I-35 in Texas. Torc is dedicated to fostering trust and collaboration with first responders, recognizing their critical role in ensuring public safety as autonomous technology becomes more prevalent. By continuing to partner with state and local agencies, Torc is committed to advancing the safe adoption of self-driving commercial vehicles, paving the way for a future where innovative technology and public safety go hand in hand.

Check out our First Responder page for more information.

Torc Announces New Engineering Center in Ann Arbor, Michigan, to Further Fuel Autonomous Vehicle Innovation

by Torc | Jun 24, 2025 | News, Featured

Sunny drone shot of the Torc Ann Arbor office location

Strategic Location Taps into Regional Automotive and Tech Talent, Builds on Recent Dallas Forth-Worth Expansion

Blacksburg, VA – June 24, 2025 – Torc, a pioneer in commercializing self-driving class 8 trucks, today announced the establishment of a new engineering center in Ann Arbor, Michigan. As Torc continues its path toward commercialization in 2027, this strategic expansion will further accelerate the company’s productization efforts and tap into the region’s rich automotive and technology talent pool.

As part of its effort to open the Ann Arbor location, Torc worked closely with the Michigan Economic Development Corporation to secure incentives to support the expansion into Michigan, as Torc plans significant hiring in this region to grow its team and technical talent capabilities.

Torc’s Ann Arbor site will help drive critical product milestones. A diverse range of engineering roles will be based in the office, including expertise in machine learning, software, hardware, and systems engineering, alongside positions in product engineering, safety, and other key functions.

“This strategic location is a natural fit for Torc as we continue to advance our autonomous technology,” said Jamie Swaim, Chief People Officer at Torc. “Ann Arbor’s proximity to the Detroit automotive industry and a wealth of high-tech talent, combined with the exceptional concentration of high-caliber universities and colleges, makes it an ideal environment for our growth and productization strategy. This new center will complement the strong engineering talent we already have across the nation.”

The new office, located in northeast Ann Arbor, will encompass approximately 32,000 square feet and will feature multiple collaboration spaces and hardware in the loop labs.

“We are pleased to support the continued growth and expansion of Torc, whose project is a testament to the strength of our state’s mobility industry and Michiganders’ superior skills,” said Quentin L. Messer, Jr., CEO of the Michigan Economic Development Corporation and chair of the Michigan Strategic Fund. “My congratulations and gratitude to Torc; we are honored to earn this investment. We look forward to celebrating your future success and that of the People, Places, and Projects who will benefit from your presence in the great state of Michigan.”

Torc’s decision to establish a presence in Ann Arbor underscores its commitment to fostering innovation and teamwork through strategic talent acquisition and collaboration within key technology and automotive ecosystems. For more information on Torc, please visit www.torc.ai.

About Torc
Torc, headquartered in Blacksburg, Virginia, is an independent subsidiary of Daimler Truck AG, a global leader and pioneer in trucking. Founded in 2005 at the birth of the self-driving vehicle revolution, Torc has over 20 years of experience in pioneering safety-critical, self-driving applications. Torc offers a complete self-driving vehicle software and integration solution and is currently focusing on commercializing autonomous trucks for long-haul applications in the U.S. In addition to its Blacksburg headquarters and engineering offices in Austin, Texas, and Montreal, Canada, Torc has a fleet operations facility in the Dallas-Fort Worth area in Texas, to support the company’s productization and commercialization efforts, as well as a presence in Ann Arbor, MI, to take advantage of the autonomous and automotive talent base in that region. Torc’s purpose is driving the future of freight with autonomous technology. As the world’s leading autonomous trucking solution, we empower exceptional employees, deliver a focused, hub-to-hub autonomous truck product, and provide our customers with the safest, most reliable, and cost-efficient solution to the market.

Large Torc sign inside the Ann Arbor, Michigan location

Aerial shot of the Torc Ann Arbor location

Torc Joins the Stanford Center for AI Safety to Conduct Joint Research on AI Safety for Level 4 Autonomous Trucking

by Torc | Jun 17, 2025 | Press Releases, Featured, News

The collaboration aims to advance autonomous trucking safety through cutting-edge AI research

Blacksburg, VA – June 17, 2025 – Torc, a pioneer in commercializing self-driving class 8 trucks, today announced its membership with the Stanford Center for AI Safety, which conducts state-of-the-art research to help ensure the safety of AI, specifically machine learning, for use in autonomous trucking applications. This membership marks a significant milestone in Torc’s ongoing commitment to ensuring the safety and reliability of its autonomous trucking solutions as the company prepares for market entry in 2027.

The membership enables Torc to sponsor, collaborate in, and coauthor research with the Stanford Center for AI Safety, enabling direct access to those research findings as they happen. Access to the center’s research symposiums, seminars, and other member benefits also help Torc apply Stanford’s extensive AI Safety research in the company’s efforts to significantly enhance the safety protocols of machine learning models within its autonomous driving systems.

“Torc is proud to join the Stanford Center for AI Safety, reinforcing our mission to deliver safe, scalable, and trustworthy autonomous solutions,” said Steve Kenner, Chief Safety Officer at Torc. “This membership aligns with our commitment to advancing rigorous safety practices in AI development and supports our goal of providing highly reliable technology to our customers.”

The Stanford Center for AI Safety’s research focuses on developing robust safety protocols and advanced machine learning techniques to mitigate risks in autonomous systems. As a member of the center, Torc can leverage published research to continue to address critical safety challenges in autonomous driving applications. Ultimately, Torc will work to continue to enhance the reliability and safety of its machine learning models toward the company’s goal of fully commercializing autonomous trucks for long-haul applications in the U.S. in 2027.

“Collaborating with members in our affiliates program allows us to apply our research in AI safety to real-world challenges,” commented Duncan Eddy, Director of the Stanford Center for AI Safety. “Our work with Torc will include efforts to enhance the safety and reliability of autonomous driving systems, ultimately contributing to the advancement of this transformative technology.”

About Torc

Torc, headquartered in Blacksburg, Virginia, is an independent subsidiary of Daimler Truck AG, a global leader and pioneer in trucking. Founded in 2005 at the birth of the self-driving vehicle revolution, Torc has over 20 years of experience in pioneering safety-critical, self-driving applications. Torc offers a complete self-driving vehicle software and integration solution and is currently focusing on commercializing autonomous trucks for long-haul applications in the U.S. In addition to its Blacksburg headquarters and engineering offices in Austin, Texas, and Montreal, Canada, Torc has a fleet operations facility in the Dallas-Fort Worth area in Texas, to support the company’s productization and commercialization efforts, as well as a presence in Ann Arbor, MI, to take advantage of the autonomous and automotive talent base in that region. Torc’s purpose is driving the future of freight with autonomous technology. As the world’s leading autonomous trucking solution, we empower exceptional employees, deliver a focused, hub-to-hub autonomous truck product, and provide our customers with the safest, most reliable, and cost-efficient solution to the market.

Advancing Safe Machine Learning: “The Community Now Owns This”

by Torc | May 29, 2025 | Education

At the recent SAE World Congress, Torc took the stage to share something big: a new safety approach to using machine learning (ML) in high-stakes areas like self-driving trucks. Paul Schmitt, Torc’s Senior Manager for Autonomy Systems, presented a paper called “The ML FMEA: A Safe Machine Learning Framework.” The work, co-authored with experts from Torc and safety partner TÜV Rheinland, addresses a major challenge in using AI for safety-critical applications: how do you know the AI is safe?

Machine learning models are often described as “black boxes”—it’s hard to see how they make decisions, and that makes it hard to ensure they’re making the right ones. As Schmitt explained during the talk, existing safety standards highlight the importance of managing risk but don’t give clear, practical tools for how to do it. That’s what inspired the team to create the ML FMEA.

ML FMEA stands for Machine Learning Failure Mode and Effects Analysis. It builds on a well-known tool, FMEA, that industries have used for decades to catch potential problems before they happen. Torc and its partners adapted this trusted method to fit the unique challenges of machine learning systems—like those used in autonomous trucks.

What makes this approach special is how it brings two very different groups—machine learning engineers and safety experts—into the same conversation. “My favorite benefit is that it gives both teams a shared language to understand and reduce risk,” Schmitt said. The framework helps teams walk through each step of the ML process and think through what could go wrong, why it might go wrong, and how to prevent it.

The team didn’t stop at the idea—they created a working template to help others put the approach into action. It includes real examples of possible failures and how to fix them, from the moment data is collected to the time the ML model is deployed and monitored in the real world.
And in the spirit of industry collaboration, Torc and TÜV Rheinland made the framework public. “We see this as a first step toward safety-certified machine learning systems,” Schmitt said. “These challenges don’t just affect self-driving trucks. They affect healthcare, manufacturing, aerospace—you name it. So we open sourced the method and template, and we’re excited to see how others improve it.”

Partnership

Schmitt also highlighted the importance of partnership: “We were thrilled to work with TÜV Rheinland on this project. Bodo Seifert instantly brought depth and credibility to the work.”

The presentation drew strong interest, with attendees snapping photos of slides and downloading the paper on the spot. During the Q&A, co-authors Krzysztof Pennar and Bodo Seifert joined Schmitt on stage to take questions. “We heard great ideas on how to expand the approach from automakers, safety experts, and standards committee members,” said Schmitt. “Seeing that level of engagement—especially from the standards community—was honestly a dream come true.”

The paper was co-authored by Bodo Seifert, Senior Automotive Functional Safety Engineer at TÜV Rheinland, Jerry Lopez, Senior Director of Safety Assurance; Krzysztof Pennar, Principal Safety Engineer; Mario Bijelic, AI Researcher; and Felix Heide, Chief Scientist.

As AI becomes more common in critical systems, tools like ML FMEA will be key to making sure it’s not just powerful—but also safe.

Register Today

for The ML FMEA Presentation Virtual Event

On Wednesday, June 11, 2025, at 1pm ET / 10am PT, four of the authors of The ML FMEA: A Safe Machine Learning Approach will be presenting on this paper as well as fielding questions. Find out more and register for the Virtual Event by visiting the presentation page.

register for the virtual event

Torc’s Approach to AI

by Torc | Apr 30, 2025 | Featured

The self-driving semi-trucks of the future have arrived – and they’re driving on highways now. At Torc, our autonomous trucks are beyond the research and development of prototypes and moving into our commercialization phase. The trucks that will solve customer needs tomorrow are online and on the road today.

Torc’s self-driving product is powered by both our cutting-edge technology and our rapidly scalable business model. Underpinning it all is responsible and expert use of Artificial Intelligence.

Artificial Intelligence (AI) is a subset of computer science, including deep learning and reinforcement learning, where software is taught to perform tasks for many different applications that would normally require a human touch or direction, like pattern recognition, computer vision, and other forms of decision making. Using AI is especially suited to designing and testing autonomous vehicles, as cutting-edge AI approaches have been shown to deliver the highest accuracy and performance for a vehicle to see, think, and act on the road.

"Training the systems to make decisions through reinforcement learning achieves optimal outcomes.”

– Felix Heide, Head of AI at Torc

AI unlocks the ability for our autonomous driving product to learn to accurately perceive and understand its surroundings, determine what others on the road may do, and safely determine the right actions to take. It ultimately makes our product more reliable and scalable on different road networks in different conditions.

Additionally, our approach to software testing also uses AI, specifically generative AI and neural rendering combined with physics models and techniques. With generative AI, we drastically increase and improve our software’s ability to drive high-volume freight routes, as well as deal with rarely encountered edge cases, allowing our autonomous driving software to experience billions of test miles in a fraction of the time and cost

The Power of Torc’s Virtual Driver

Driving is a skill that people never stop developing. Every time we put the key in the ignition, we learn from our roads and our fellow drivers. We’re constantly tweaking and refining our own driving behaviors with time and experience – and so should our machines.

Enter AI. AI is what powers our software to pick up on changes in the world around it and adjust accordingly, in real-time. For example, whether it’s the ability to pause at a newly installed stop sign or notice construction cones modifying where it is safe to drive, AI powers how the scene is seen and understood, and how the truck should behave, to be the safest vehicle on the road. Its use in self-driving applications cannot be understated. AI can both teach and learn driving skills in less time – specifically what computers were invented to do.

We refer to our AI software suite as Torc’s Virtual Driver: our advanced approach on seeing, thinking, and acting for self-driving trucks. Torc’s Virtual Driver combines cutting edge end-to-end learning and verifiable AI with algorithmic redundancy, allowing Torc to quickly evolve and scale to interpret the world around it, as well as adapt and absorb new sensor technologies and customer routes.

You Might Be Interested In: Daimler Truck’s Autonomous-Ready Fifth Generation Freightliner Cascadia Hits Texas Roads With Torc

Achieving Safe and Accurate Perception, Planning, and Prediction

To train and verify the models within the Virtual Driver, and make our technology as robust as possible, Torc uses novel generative AI approaches for our software testing. It assesses the Virtual Driver by driving billions of miles in every conceivable circumstance (more miles than a human would ever be able to drive in their lifetime) in simulation before it hits the road, and making changes as needed in real-time. AI isn’t just saving us time – it’s saving lives by helping us create situations most humans would never encounter, such as various weather conditions, unexpected pedestrian or vehicle behaviors, and many other edge cases to deeply test that the Torc Virtual Driver works correctly in all conditions and scenarios.

“Overnight, we can create a massive number of heavily optimized scenarios with parallel conditions that we can use for training very easily. This also allows us to scale the data we need much more easily for verification and validation of the software,” says Felix Heide, Head of AI at Torc. “It is possible to train software models and perform object detection without any training data on completely unseen, unannotated sequences of frames. We can use any real-world videos from a vehicle dash cam or test vehicle and use AI to match objects in an image frame, create our own settings, and finally derive a myriad of scenarios in a novel way to get away from the heavy reliance on limited training data sets. Training the systems to make decisions through reinforcement learning achieves optimal outcomes.”

Together, the modular AI powered Virtual Driver, taught and tested by next-level generative world simulation AI and further validated in the real-world, elevates Torc’s AI approach to the next level.

Torc’s technology unlocks high-performance verifiable AI, what we call AV 3.0, the highest performance and safety measures in the industry. With real world plus generative AI data loops working together, Torc’s AV 3.0 approach on our production embedded hardware enables fast and predictable product scaling for our fleet customers.

Partnerships Make It Happen

Importantly, our technology is built on best-in-class industry partnerships. Our Virtual Driver runs on the Flex Jupiter high-performance embedded compute platform, powered by NVIDIA DRIVE AGX™ technology. It is factory-integrated within the industry’s first and only autonomous-ready Class 8 truck chassis, the 5.0 Freightliner Cascadia developed by Daimler Truck, providing highest reliability and volume for our fleet customers.

Taken individually, it’s impressive. Together, it’s unequaled. We’re offering our customers unparalleled levels of redundancy and high reliability. And with the Freightliner Cascadia already owning nearly 60% of the U.S. Class 8 long-haul trucking market share today, this product and this partnership cannot be matched.

Torc has the best power, performance, and cost advantages for freight companies moving to adopt self-driving trucking. Our strong collaborations ensure autonomous trucks can be quickly produced at scale to meet manufacturing and market demands by our customers.

You Might Be Interested In: Torc Collaborates with Flex on Physical AI Platform for Autonomous Trucks, Accelerated by NVIDIA

Our AI-powered simulation
make us fast, flexible, and efficient.

AUTONOMY FORWARD

Torc’s differentiations are distinct and our customer engagements prove we’re on the right path to delivering the right self-driving product at the right time. We’re combining all the pieces in the right order:

Our deep integration with Daimler Truck, building the autonomous-ready Freightliner chassis for safer, more reliable, day-to-day autonomous freight operations
Cutting-edge AI – AV 3.0 – providing end-to-end self-driving capabilities that are safe, scalable, and adaptable for US road networks
The embedded automotive-grade hardware needed to run Torc’s autonomous software, allowing for reliable real-time operation in harsh environments

But that’s not the end of the story. We’re not just working on the software and hardware. Our commercialization team is working closely with fleets today to understanding their needs tomorrow, their existing network infrastructure, and their pain points, so that we have the right application and tooling when the autonomous trucks are on the road.

The road to 2027 is paved with groundbreaking advancements, and with the support of AI technology, market and industry leaders, and the best team in the business, we’re driving toward a future where autonomous trucking transforms the way goods move across the world. Torc is driving the future of freight.

Our use of AI is governed internally by a cross-functional committee. The Generative AI Committee is dedicated to achieving Torc’s business objectives while adhering to the company’s commitment to Do the Right Thing. The Generative AI Committee’s mission is to establish a program to ensure AI systems used across Torc business functions conform with enterprise values, policy compliance, regulatory standards, and industry best practices.

From engines to algorithms: Gen AI in automotive software development, January 2025, https://www.mckinsey.com/features/mckinsey-center-for-future-mobility/our-insights/from-engines-to-algorithms-gen-ai-in-automotive-software-development. AI tools have revealed a “productivity improvement of 44 percent when using gen AI with quality assurance measures, such as creating and automating tests to then enhance efficiency and code reliability.”