Breakthroughs in AI for autonomous driving do not happen in isolation. They happen when data, simulation, machine learning, infrastructure, and vehicle testing work as one integrated engine for innovation. Recognizing that, Torc created the DataLoop and Simulation (DL+S) division in the AI organization to connect every link in the development cycle and accelerate progress at scale.
As Torc progressed toward its AV 3.0 vision, we recognized the need to unify data ingestion, data processing, experimentation, training, validation, and deployment across the organization. Achieving that scale required both organizational alignment and technical infrastructure capable of orchestrating massive machine learning workloads. The maturing of an autonomous driving organization required an investment in centralization and in standardized tooling and led Torc to standardize on technologies such as Ray by Anyscale as part of its broader DataLoop strategy.
This approach has required substantial upfront investment and cultural buy-in, but the long-term benefits are considerable. We have already seen how standardized workflows improve development velocity by enabling faster support and maintenance code, thereby reducing duplication of effort. Common tooling enables better auditability and repeatability, which are essential for safety-critical systems. Shared platforms also allow teams to collaborate more effectively instead of rebuilding similar capabilities multiple times.
The organization supports the complete lifecycle of autonomous driving development through what Torc refers to as the DataLoop.
Understanding the DataLoop
At its core, the DataLoop is a continuous cycle of learning, validation, and improvement.
Closed-Loop Autonomous Truck Data Lifecycle
The process begins with data ingest, where new information arrives from vehicle operations, testing, and other sources. Each Torc truck mission produces roughly 9TB/hour of new data, all of which must be managed in perpetuity to satisfy safety standards. That raw data then moves through data processing, where it is supplemented with the appropriate for use in machine learning and simulation.
Next comes model development and model validation, where engineers train and refine AI models as part of model experimentation. Experimentation is not linear, but instead a loop that constantly uses new insights gained from model validation to guide development. Autonomy teams continually evaluate results, adjust model architecture and hyperparameters, augment datasets, and repeat the process to improve performance. DL+S is responsible for accelerating this experimentation loop, both through faster computation and better UI/UX to surface and share developer insights.
Once a model demonstrates promising results, it enters convert & deploy, where it is prepared for operation on our production hardware. This requires an auditable workflow from TensorRT and eventually a TorcDrive cwhich can undergo final performance and latency validation. Finally, a new stack release which is deemed performant and safe undergoes truck testing, which renders the final safety validation step. We both celebrate our progress and surface.
How Much is 9TB/Hour of Data?
Trying to figure out how much 9TB of data is?
9 TB is about 2,250,000 songs worth of data, or 2.5 million standard smartphone photos. And it’s almost a tenth of the entire printed collection of the United States Library of Congress.
Torc trucks are producing that amount of data per hour.
“It is our job, at DataLoop + Sim, to recognize these challenges, implement scalable solutions, and partner with our Autonomy colleagues to drive the technology forward.”
What Makes Autonomous Trucking Difficult?
- We need to design and implement a data format that is both rigid enough to scale, but also flexible enough to accommodate future sensor/hardware changes.
- We need auto-labeling processes, hand-checked by our manual labeling team, to identify useful data and process it for training.
- We need training frameworks that can effectively balance the compute task to the right computer power, to train on this data in hours, not days.
- Lastly, we need simulation, at all levels of fidelity for truck-specific motion models, to both power our reinforcement learning process and confirm that the model we trained will have suitable post-conversion performance as a deployable TorcDrive component.
The Role of Simulation Throughout the Loop
Simulation is embedded throughout several stages of the DataLoop, which is why it is called out separately in the division’s name.
DL+S uses simulation to support synthetic data generation, enabling teams to create validation scenarios that may be difficult, expensive, or time-consuming to capture in the real world. Simulation is the engine of reinforcement learning, allowing models to learn from large volumes of virtual interactions and gain hundreds of years of driving experience in hours. And, for all autonomy teams, model validation through replay and recompute workflows helps developers analyze prior vehicle behavior, compare against changes, and test improvements under controlled conditions.
Especially in the context of model validation, every investment in improving our simulation capabilities gives us more ability to operate efficiently. Higher fidelity models give better estimates of truck performance and maximize the value of physical truck launches and tests by excluding new model versions which are insufficiently performant. Lower fidelity models offer faster ability to perform simulation and explore the domain of possible circumstances worth further study.
Four Teams
Torc’s DL+S organization brings together four tightly connected teams:
Simulation
Simulation is, unsurprisingly, responsible for building our in-house sim tooling and ensuring its successful use in synthetic data generation, reinforcement learning, and validation. This requires both research into high fidelity truck motion simulation and implementation of differentiable simulation to facilitate training at a pace approaching 1 million steps/second.
Data Ops
Data Ops is responsible for all of the ingestion and maintenance of truck data at Torc. When we drive a data collect mission, buy a dataset, or generate synthetic data, or when we add labels to data, either manually or with auto-labeling models, they manage it. When we conduct a simulation, they store the outputs and make them available for subsequent processes.
ML Ops
ML Ops has the broad mandate of working to ensure compute infrastructure exists for the entire AI Org, alongside the appropriate frameworks for efficient usage of that infrastructure. This includes training computer vision models, reinforcement learning, batch inference, KPI dashboarding, and model conversion.
ML Data
The DataLoop for Today and the Future
By standardizing tooling, improving auditability, enabling repeatable experimentation, and supporting ever-larger multimodal AI workloads, DL+S transforms individual engineering efforts into a unified development system. As Torc continues advancing toward fully autonomous trucking, the DataLoop serves as the engine that continuously converts new data into better performance on the road. We strive to maximize learning/minute.
But the success of DL+S demands more than digital infrastructure. Accelerating development is not a purely technological goal, it is also a culture goal. Its purpose is to create a common foundation of tooling and practices that enables every autonomy team at Torc to experiment quickly without sacrificing rigor. To achieve the company’s goals, Autonomy and DL+S must share a vision of the future and jointly work towards it. Training sessions, customer discovery interviews, embedded solutions engineers, shared offsites, video happy hours, late nights in Dallas jointly debugging the double lane change… we have built a rhythm that helps us do the hard work of identifying the shared path forward and choosing to pursue it together.
I am proud of the efforts of everyone in the organization. We are working not just to improve our software but also to improve the process of building that software. That effort will pay dividends for the immediate future and beyond, empowering us to “Drive the Future of Freight” more efficiently than ever.
Anyscale by Ray: Efficiency and Scalability of Compute
Many organizations focus on raw compute availability to scale workloads, but the real challenge is using compute effectively. Large multimodal training jobs require balancing CPU resources, GPU resources, and data throughput simultaneously. Without efficient orchestration, expensive infrastructure can sit underutilized while training workloads wait for data or processing bottlenecks to clear.
With Ray, Torc focuses on optimizing utilization rather than simply increasing capacity. shifting the focus from “Can I get more hardware?” to “Am I using the hardware effectively?”
The impact has been measurable. Recently, the team optimized a key workload for the scene modeling truck component; it provides information to the behavior models about nearby objects such as vehicles and pedestrians. They were able to improve a 4 hour training time to 1.5 hours, dropping time and costs by 73%, by improving data flow and using cheaper compute. That outcome captures the broader transformation: Ray helped move the organization from simply executing large workloads to optimally executing large workloads while improving quality.
Felix Heide presenting AV 3.0 at the 2026 Ray Summit, by Anyscale. Click below to watch his keynote address.