CVPR 2026 Recap: Torc’s Mission Intersects With Event Trends of Physical, Multi-Modal AI, Deployable Systems

CVPR 2026 Recap: Torc’s Mission Intersects With Event Trends of Physical, Multi-Modal AI, Deployable Systems

by | Jun 11, 2026 | AI, Featured

Advancing the Future of Autonomous Driving

CVPR 2026 marked its largest edition to date, bringing together more than 10,000 attendees and setting a new record with 16,092 paper submissions. With an acceptance rate of just 25%, the conference continues to maintain its reputation for rigor and selectivity even amid rapid growth.

This year’s event highlighted several defining trends in computer vision and AI. Multi-modal AI emerged as a major focus, doubling its presence among top papers year over year. At the same time, research in 3D and 4D reconstruction, world models, and autonomous driving saw significant momentum, signaling a broader shift toward systems capable of understanding and interacting with complex, dynamic environments.

These trends align closely with Torc’s mission.

A Strong Presence on the Global Stage

As a platinum sponsor alongside partner NVIDIA and other industry leaders, we were proud to represent the cutting edge of autonomous freight vehicle innovation.

Our booth welcomed hundreds of visitors, where attendees explored one of our autonomous Freightliner Cascadia trucks and experienced our immersive VR demonstration. Guests also had the opportunity to engage directly with Torc’s experts, including Head of AI Felix Heide and members of our AI team and engineers, for in-depth discussions on physical AI and the future of autonomous systems.

Research Contributions

Torc’s presence at CVPR extended beyond the show floor through research and thought leadership:

Looking Ahead

CVPR 2026 underscored a clear turning point in AI: A shift from perception-centric models to multi-modal, generative, and embodied systems capable of full 3D world understanding and real-world deployment.

We’re proud to be at the forefront of this technology and grateful to everyone who connected with Torc during the event.

Stay connected with Torc on social channels to see where we’ll be next.

From Black Box to Glass Box: AV 3.0, Physical AI, and the Future of Long-Haul Trucking 

From Black Box to Glass Box: AV 3.0, Physical AI, and the Future of Long-Haul Trucking 

by | May 28, 2026 | Featured, Technology

In 2025, Torc introduced AV 3.0, our framework for building autonomous software that is safe, scalable, and designed specifically for long-haul trucking autonomy. It powers TorcDrive, the physical Al virtual driver software that perceives, reasons, and navigates the real world in real-time.

We’ve had a lot of questions about our AV 3.0, evolution and how it differs from, and surpasses, the autonomous software industry as a whole. Here’s a breakdown of all the predecessors to our AV 3.0, what makes TorcDrive different and distinctive, and why we believe it sets the benchmark for state-of-the-art ADS systems.

Rules-Based Approaches and AV 1.0: Open Books and the First Learned Models

It all began with rules-based, open book software. In 2007, the Torc team VictorTango drove Odin, a 2005 Ford Escape hybrid designed to navigate complex urban environments without human intervention. The system successfully drove through roundabouts, intersections, and cross-traffic using a rule-based architecture, where the vehicle behavior was governed by pre-programmed, deterministic logic (if-then statements). Such early systems were highly interpretable and traceable. Each decision could be linked back to a specific rule, sensor input, or subsystem. This made it possible to diagnose failures with precision. In the mid-2010s, the emergence of deep learning enabled the initial move to AI with a massive improvement in perception performance, allowing vehicles to better detect objects, understand lane structure, and interpret complex scenes. This is what we refer to as AV 1.0, a hybrid approach combining learned perception models with rule-based prediction and planning. These architectures operated on inputs from cameras, lidar, radar, high-definition maps, and hand-coded rules of the road. However, these systems were inherently brittle, since downstream behavior prediction and planning components remained largely hand-engineered. It’s nearly impossible to imagine every scenario in advance and code appropriate rules to handle it. As a result, this architecture, combining first-generation learned perception models with rules-based prediction and planning, were not capable enough for the complexity of fully autonomous driving. Operating with distinct rules in silos, AV 1.0 vehicles were often stymied by simple situations that a human would negotiate without a second thought, like a misplaced construction cone or worn-away lane markings. AV 1.0 was far from being a scalable commercial product that could replace drivers.
Fear of a Machine You've Never Met

Most Americans are anxious about how quickly AI is advancing. That anxiety is especially pronounced with physical AI—machines that use sensors to perceive, reason, and move around in the real world in real time—like TorcDrive. But few Americans have directly interacted with any Physical AI systems.

According to polling, 87% of Americans have never ridden in an autonomous vehicle, or even know someone who has.

Not All Autonomy Is the Same

Much of society is limited to forming opinions based heavily on eye-catching headlines hyped by algorithms eager for clicks and eyeballs. All of us benefit from learning about the fundamental differences between the powerful physical AI quietly driving autonomous freight trucks and the AV systems in private vehicles and robotaxis making the evening news.

AV 2.0: A Magic Black Box

In the last five years, we’ve seen a massive leap in vehicle capabilities with AV 2.0 end-to-end learned platforms. This is one of the leading directions in AV research, moving beyond heavily rule-based planning and map-dependent architectures toward unified learned systems. Instead of relying on hand-designed rules for each part of the driving stack, AV 2.0 driving solutions leverage advances in deep learning, generative modeling, and large-scale data to train models that can run in-vehicle and connect perception, prediction, planning, and control within a single learned framework. Like the large language models we increasingly use every day, these systems are capable of impressive results. But that capability comes with a major tradeoff: opacity. In a highly integrated end-to-end model, it can be difficult to determine why the vehicle behaved a certain way, where an error originated, or how to make a targeted correction without affecting other parts of the system. A change intended to improve behavior in one scenario can introduce regressions elsewhere, requiring large-scale retraining, validation, and testing. This makes development more expensive, slows iteration, and creates uncertainty around how behavior may shift from one model update to the next. For long-haul trucking, the challenge is even greater. Safe physical AI for heavy trucks requires more than what is sufficient for robotaxis or passenger vehicles. Trucks operate with longer stopping distances, reduced maneuverability, different visibility constraints, and a need for long-range perception, prediction, and planning. The next generation of autonomous trucking must combine the deterministic confidence and traceability of rule-based autonomy, the modular structure and debuggability of AV 1.0, and the performance gains of the end-to-end approach in AV 2.0.

AV 3.0: The Transparent Glass Box

The next generation of autonomy should not force a choice between the traceability of rules-based systems and the performance of modern end-to-end AI. Torc’s approach combines modular approaches, deterministic guardrails, and end-to-end training into what we call AV 3.0: a transparent architecture for autonomous trucking. Rather than a black box approach to autonomy, AV 3.0 is designed as a “glass box”, a system whose major components can be inspected, validated, and improved with precision. This approach is structured to provide visibility into how the truck perceives the world, predicts the behavior of other road users, and selects a safe driving plan. To develop AV 3.0, Torc has the high-powered simulation and data loop needed to train, test, and validate TorcDrive before we deploy it on public roads. The result is a scalable autonomous trucking software product designed for the complexity, safety requirements, and operating realities of long-haul freight.
A graphic describing the three parts of Torc's AV 3.0

1. A Verifiable AI stack

TorcDrive automated driving software is organized around three core functional modules: Perception, Prediction, and Planning. Together, these modules allow the system to understand the driving environment, anticipate how surrounding actors may behave, and generate safe driving plans for the truck. This forms a modular learned software stack that is learned in an end-to-end manner, and is supported by deterministic, rule-based guardrails and clearly defined safety criteria. This modular structure makes the system more inspectable, testable, and verifiable than a single black-box end-to-end model. Engineers can examine intermediate outputs from each stage of the stack and validate whether the different components are responding appropriately to the input. When changes need to be made, Torc can target specific elements of the stack, validate at the module-level output, and then test the full system in closed-loop conditions before deployment. Equally important as the modular structure of the AI stack is how TorcDrive is trained and validated.

2. Immersive AI Training and Validation

Before it reaches the road, TorcDrive is trained and tested through a large-scale data and simulation loop designed to expose the system to the complexity of real-world trucking. This can be seen as the autonomous trucking equivalent of a CDL driver-education program, except the system is specifically tested in rare, complex, and safety-critical scenarios at a scale no human driver could encounter in a lifetime. Torc accomplishes this using our proprietary generative simulator trained on real-world data captured by production-intent sensor suites on over-the-road trucks. Torc’s generative AI and data-loop environment creates a large and continually expanding set of challenging driving scenarios grounded in real-world data and physics-based modeling. These scenes include cars, trucks, pedestrians, animals, varied road geometries, changing conditions, and rare edge cases that are difficult to collect repeatedly through road testing alone. Our immersive training environment accelerates TorcDrive’s ability to learn new routes and autonomous hub layouts, allows it to quickly adapt to regional driving conditions, and prepare for challenging scenarios such as severe weather, unexpected pedestrian encounters, or rare vehicle behaviors. Simulation does not replace real-world validation, but it greatly expands the range of situations TorcDrive can experience before deployment.

3. Seamless Hardware Integration

In 2019 Torc Robotics became an independent subsidiary of Daimler Truck NA, the world’s largest and leading OEM, bringing together extensive experience in freight industry manufacturing and relationships with Torc’s experience in developing autonomous vehicle solutions. This is the first strategic alliance between an autonomous vehicle technology firm and a truck original equipment manufacturer (OEM), gathering all of the essentials needed to create a scalable Class 8 SAE Level 4 truck. Together, Torc and Daimler Truck have developed a truck purpose-built for fully self-driving autonomous operation in long-haul trucking and production at scale. It combines a Verifiable AI stack hosted on a proprietary NVIDIA-powered embedded compute platform provided by Flex, with sensors, other hardware, and Daimler’s autonomous-ready 5.0 Freightliner Cascadia. This chassis is specifically designed for SAE Level 4 autonomous operation, with all the essential compute stack components and sensors installed on the production line. Complete with necessary redundancies for safety-critical components, the vehicle and platform have been proven and validated for highway operations. This is the first autonomous freight vehicle to fully transition to a production-ready, purpose-built platform—the autonomy validated from the wheels up from the beginning, not bolted-on after the fact.

Riding Along as the Future Unfolds

Over the coming weeks, we’ll be sharing more detailed pieces of our product and how it has been developed on the Al-powered technology wave. What’s down the road? Connect with us to see what’s next.

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Torc Robotics Announces First-Ever Autonomous-Trucking Partnership at Mila to Advance Physical AI

Torc Robotics Announces First-Ever Autonomous-Trucking Partnership at Mila to Advance Physical AI

by | May 26, 2026 | Featured, News

Hoods of Torc modified Freightliner Cascadias

Montréal, QC and Blacksburg, VA, May 26, 2026 — Torc Robotics, a pioneer in self-driving vehicle technology, today announced a new strategic partnership with Mila – Quebec Artificial Intelligence Institute, one of the world’s leading centers for machine learning research.

Through this collaboration, Torc will establish a presence within Mila’s ecosystem in Montreal, becoming the only autonomous trucking company to join the institute, and gaining access to top-tier academic talent, including students, researchers, and faculty. The partnership also includes dedicated research space on site and is designed to build on Torc’s existing AI and autonomy research to deepen its capabilities in physical AI through direct collaboration with Mila’s faculty and researchers.

Mila is globally recognized for its contributions to machine learning and applied AI research, with a large community of researchers, strong ties to leading universities in Canada, and a reputation as a launchpad for top AI talent, with alumni and affiliates holding leadership roles in well known companies such as OpenAI and Google.  By embedding within Mila’s collaborative environment, Torc will deepen its research capabilities in emerging areas of autonomy, including generative world models, multi-agent behavior modeling, reinforcement learning, and foundation models for physical AI systems.

“Torc is focused on building safe, scalable autonomous trucks, and advancing the next generation of physical AI is central to that mission,” said Felix Heide, Head of Artificial Intelligence at Torc. “As a long-time Mila collaborator, I can definitively say that partnering enables deeper collaboration at the intersection of research and real-world deployment, collaboration that supports continued progress toward commercializing autonomous trucking at scale.”

“We are excited to welcome Torc as an industry partner, as it becomes an even stronger component of Mila’s ecosystem,” said Christopher Pal, Core Academic Member at Mila, Scientific Co-Director of IVADO and Professor at Polytechnique Montréal. “This partnership brings together academic excellence and real-world deployment, creating opportunities for our students and researchers to work on impactful challenges in physical AI while advancing the state of the art in autonomous systems.”

The partnership builds on Torc’s existing presence in Montreal and an affiliation with Mila that dates back to 2020, reinforcing its commitment to investing in global AI talent and research partnerships. Together, Torc and Mila will explore new approaches to physical AI that bridge simulation and real-world performance, helping to unlock safer and more efficient autonomous transportation.

“As autonomous vehicle technology becomes closer to a reality, it is exciting and important to see new collaborations between academic labs and top tier companies that are bringing the technology to market,” said Liam Paull, a Core Academic Member at Mila, a Canada CIFAR AI Chair, and an Associate Professor at Université de Montréal, where he co-leads the Montréal Robotics and Embodied AI Lab (REAL).

About Torc

Torc is driving the future of freight with autonomous technology. Torc has more than 20 years of experience in pioneering safety-critical, self-driving applications. Torc offers an AI-forward, 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 Ann Arbor, MI, and Montreal, Torc has a fleet operations facility in Dallas-Fort Worth, to support the company’s productization and commercialization efforts for our customers.  As an independent subsidiary of Daimler Truck AG, a global leader and pioneer in trucking, Torc is empowering exceptional employees, delivering a customer-focused autonomous truck product, and providing the safest, most reliable, and cost-efficient solution to the market.

 

About Mila – Quebec Artificial Intelligence Institute

Founded by Professor Yoshua Bengio, Mila – Quebec Artificial Intelligence Institute is the world’s largest academic AI research center specialized in deep learning, home to a community over 1500 members strong. Based in Montreal, Mila was created out of a unique partnership between Université de Montréal and McGill University, dedicated to advancing scientific breakthroughs that drive innovation and ensure AI benefits everyone. A non-profit organization, Mila is strongly supported by the Government of Canada through the Pan-Canadian AI Strategy and by the Government of Quebec. Internationally recognized for its influential research, global innovation partnerships, and leadership in multilateral efforts on responsible AI, Mila continues to shape the future of AI worldwide. For more information, visit mila.quebec.

 

 

The autonomous ready Freightliner Cascadia, with Torc virtual driver software embedded within the chassis.
ACT Expo 2026 Recap: Two Torc Trucks Are Better Than One 

ACT Expo 2026 Recap: Two Torc Trucks Are Better Than One 

by | May 14, 2026 | Featured, News

ACT Expo 2026 once again brought together the most influential voices in commercial transportation in Las Vegas. As North America’s largest advanced transportation technology event, the May 4-7 event served as a key meeting point where innovation, policy, and real-world deployment converged. 

Torc trucks were front in center in not one booth, but two. Penske Transportation Solutions and Torc’s parent company, Daimler Truck, were both event presenting sponsors, and each took the opportunity to showcase Torc autonomous-ready Freightliner Cascadia vehicles, featuring TorcDrive, in their booths. 

“I think the hurdles have shifted,” said Peter Vaughan Schmidt, Torc CEO, during the Virtual Driver Revolutionizing Trucking and Logistics panel on Tuesday, May 5. “In the past, it was really proving that your technology is really working. And as things come together both with compute and with range and end to end AI, I think the real hurdle is proving to customers that it seamlessly fits and provides a better fit for them — that you can run autonomous trucks from point to point, not just hub to hub.” 

Watch the full session here:

“We are the only ones that actually run on production-intent hardware and software. That’s needed for scale… at low cost, high quality and high volume,” said Peter. “That’s our race to produce a safe and scalable product for our customers.” 

The 2026 event spotlighted the technologies shaping the future of fleets, including autonomous vehicles, battery-electric, hydrogen fuel cell, and near-zero emissions solutions, along with the infrastructure and software required to scale them. Industry leaders, OEMs, fleet operators, utilities, and policymakers shared insights focused on accelerating the transition to cleaner, more efficient transportation. 

Beyond the exhibit hall, ACT Expo 2026 offered in-depth educational sessions covering total cost of ownership, charging and fueling strategies, regulatory updates, and real-world case studies from fleets already leading the transition.  

With collaboration and innovation at its core, ACT Expo 2026 helped fleets conceptualize and jumpstart the move from ambition to action, and Torc was there as a physical representation of that action, twice over. 

Panelists at the ACT Expo 2026 Virtual Driver Revolutionizing Trucking and Logistics session

Image courtesy of ACT Expo

Digital AI vs. Physical AI: What’s the Difference and Why It Matters

Digital AI vs. Physical AI: What’s the Difference and Why It Matters

by | Apr 15, 2026 | Education, Featured, Technology

Have you used AI to streamline your work or generate a list for you? Have you been fooled by an AI image? The technology we’ve been watching in movies for 20 years is now in our homes, cars, phones, and schools. Artificial intelligence is no longer theoretical. Most people—across industries and age groups—interact with tools like ChatGPT, Claude, or Gemini. Work apps like Zoom now offer AI summaries, and Microsoft and Adobe have generative AI tools built into their products. This widespread exposure has created a shared baseline: AI is useful, real, and improving quickly.

But there’s a critical distinction that’s still not widely understood—digital AI vs. physical AI.

Digital AI:

Intelligence On-screen

Digital AI operates in a purely virtual environment. It processes text, images, and data to:
• Answer questions
• Generate content
• Analyze patterns
• Support decision-making

Its rapid rise wasn’t accidental. Digital AI benefited from a massive, ready-made dataset: human language and images. For decades, we’ve been writing, storing, and digitizing information—emails, documents, books, photos, websites. It’s been trained on the Internet: the largest repository of language and visuals in human history.

Once that data became accessible, AI systems could be trained quickly and at scale.

That’s why digital AI feels like it “appeared overnight.” It was trained “in the cloud” of ever-expanding data centers, allowing for rapid iteration.  

Physical AI:

Intelligence in the Real World

Physical AI takes the same foundational technologies as digital, on-screen AI—machine learning and neural networks—and applies them to real-world interactions.

Instead of predicting the next word, physical AI must decide:
• What object am I seeing?
• How do I move around safely?
• How hard should I grip this item?
• What action should I take next?

This introduces massive complexity. Instead of just structured data, physical AI must work within the real, unstructured environment, around people and things that don’t always follow the rules. The real world isn’t just data—it’s dynamic, unpredictable, and only governed by physics.

Physical AI needs to account for the 3D space of dimensions, with sensors, actuators, and an understanding of interactions and their outcomes.

Why Physical AI Is Slower to Scale (for Now)

When we built digital AI systems, we chose the right dataset by only using some parts of the internet. Chatbots were built using mostly social media data; Coding Agents were built using mostly open source code data.

Physical AI also requires special data for real world use cases. The world is extremely complex, and the sensors that “see” it can also be complex. But, this time, we can’t just look on the Internet to “find” it. The sensors we use, the environments in which robots move, and the special situations we have to prepare for have simply not been recorded.

Unlike digital AI, physical AI lacks a rich historical dataset like the Internet. There’s no ready made data set for how humans move through environments, how objects behave under force, or how tasks are physically completed. All the physical data must be created, not just collected.

The same generative tools that you use in digital AI can be applied within a simulated world, using real-world, grounded physics, and the physical AI hardware (robots and sensors) can be built alongside the AI. We don’t always have to wait for a robot to go out into the world, record the data, and bring it back to us – the robot isn’t ready yet! Companies like NVIDIA, Tesla, and Amazon are accelerating this by combining compute power, simulation, and real-world data collection.

At Torc, our autonomous driving system, TorcDrive, is being trained to work in the real world using both real-world data (recorded camera and lidar images of on-road driving) as well as complex simulated images created from those same on-road recordings, simultaneously.

The Key Insight: Same Brain, Different Body

The most important takeaway is this: digital AI and physical AI are built on the same core technology. Digital AI is proving what’s possible and adaptable by humans, and most importantly, helpful. Physical AI extends that capability into the real world. Everything you’ve seen AI do on a screen—learning, adapting, improving—will eventually happen in the physical world.

What This Means for the Freight Industry

For freight, logistics, and operations, digital AI helps companies and people think better … physical AI can help you perform better.

At Torc, physical AI is manifested in our autonomous driving software, TorcDrive, and powered by AV 3.0, on our trucks today, after being trained by millions of hours of real world and simulated scenarios. It’s the realization of the promises of hundreds of years of technology and human invention. We are still in early days of building physical AI but the trajectory is clear. Digital AI and physical AI aren’t separate revolutions. They are one and the same.

Meet Your First Physical AI

A physical AI machine you might be familiar with is the in-house robot vacuum. The simplest vacuum has the most basic sensors to register the world around it. If it bumps into something, the computer registers the bump against the moveable panel and turns the wheels to rotate itself in a different direction. Other more expensive models can use sensors to determine floor material, and then adjust how they clean accordingly.

More Going On Under the Hood

There are robot vacuum models with self-emptying functions, pet sensors, and even self-cleaning controls. One of the newest models introduced at CES 2025 even has a robotic arm to pick up socks. As we transition to a more physical AI world, how physical AI “understands” and interacts with its surroundings is very different “under the hood” (or dust bin, in this analogy). It’s becoming more complex, and smarter, and importantly, more applicable and helpful.

Not All Physical AI Thinks the Same

How different physical AI instances and machines “think” and how they must act on their sensors vary widely. It is critical to keep in mind that systems are running ever-increasing AI models, trained on real world and simulation data, designed to interact with the physical world in more capable ways, far exceeding the scalability (and capability) of the first physical AI technologies.

Freight Industry Physical AI Key Applications and Workflows
  • Autonomous Warehouse Operations
  • Intelligent Fleet Management & Safety
  • Dynamic Load Optimization
  • Automated Material Handling
  • And of course … Autonomous Vehicles
AV3.O

Read More:

 

Torc Supports GO Virginia–Funded Effort to Align Autonomous Vehicle Workforce Training Across the Commonwealth 

Torc Supports GO Virginia–Funded Effort to Align Autonomous Vehicle Workforce Training Across the Commonwealth 

by | Mar 10, 2026 | Featured, News

Torc and Dock 2 Door VTTI team in front of Torc trucks on the Smart Road

Torc contributes industry expertise to VTTI-led Dock to Door Pathways Program focused on AV inspection and credentialed career pathways

BLACKSBURG, Va – March 10, 2026 – Torc, a pioneer in commercializing self-driving class 8 trucks, today announced its participation in a newly awarded GO Virginia Region 2 planning grant led by the Virginia Tech Transportation Institute (VTTI)’s Dock to Door Coalition (D2D). The one-year grant will support planning efforts to align university and community college curriculum with evolving workforce needs across the autonomous vehicle manufacturing ecosystem.

The initiative is designed to lay the groundwork for the future D2D Pathways Program, which would streamline training programs across Virginia to prepare students and mid-career professionals for in-demand roles — including inspection and safety-critical positions supporting autonomous commercial motor vehicles. The autonomous manufacturing sector is the second largest in Virginia’s Region 2, thus, opportunities to specialize and upskill are critical to staying on pace with industry growth.

As an industry partner, Torc is contributing subject matter expertise to help identify core competencies, training recommendations, and credentialing opportunities required for inspectors and technicians working with autonomous trucks. This includes aligning curriculum concepts with nationally recognized inspection and safety frameworks (such as CVSA) and defining career lattices that connect entry-level credentials to mid- and advanced-level roles.

“The autonomous trucking industry is rapidly advancing, and we recognize a strong need for trained experts in the field,” said Anita Kim, director, state government and regulatory affairs at Torc Robotics. “By working alongside VTTI and the Dock to Door Coalition, we’re helping ensure that education and training pathways reflect the skills needed to support safe autonomous trucking operations — and that those pathways lead to sustainable jobs here in Virginia.”

The planning grant brings together industry, academic, nonprofit and public-sector stakeholders through the Dock to Door Coalition, a network of more than 90 partners spanning the supply chain. The effort will focus on mapping existing programs, identifying gaps, and recommending pathways that support both autonomous and electric vehicle manufacturing and operations.

“This work is about translating industry demand into actionable training pathways,” said Kaitlyn Bedwell, project lead and a team leader within the supply chain, transportation, automation and resource sustainability team at VTTI. “As new policies and license requirements emerge, working alongside Torc, which is on the frontline of industry innovations, will help our students and future engineers stay ahead of the curve.”

The GO Virginia Region 2 planning grant began on November 15, 2025, and will run for one year. Findings from the effort are expected to inform a future implementation phase focused on deploying scalable, industry-aligned workforce training programs across Virginia.

 

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 industry, 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 Ann Arbor, MI, and Montreal, Torc has a fleet operations facility in Dallas-Fort Worth, to support the company’s productization and commercialization efforts. 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.

About the Dock-to-Door Coalition

The Dock to Door (D2D) Coalition, led by the Virginia Tech Transportation Institute, is a 90+ member partnership uniting industry, government, higher education, and non-profits to build a fully connected, resilient, and sustainable freight transportation system. The coalition accelerates next-generation supply chain innovation through four core program areas that improve safety, visibility, efficiency, and workforce readiness as it relates to advancing multimodal automation, from long-haul trucking to last-mile delivery—while expanding benefits to rural and suburban regions through strengthening of regional talent pipelines.

Two Torc trucks on the Smart Road in Blacksburg Virginia