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.
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.
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.