Driving the Future: Spotlighting the Torc Machine Learning Frameworks Team
Torc’s autonomous software system is constructed in part from machine learning and artificial intelligence components. The Torc Machine Learning Frameworks team is creating the software stack which learns from the data collected by our fleet of trucks in on-the-road testing. This group of engineers is responsible for the automated training of machine learning models, and then the automated testing and deployment to our embedded hardware.
“Our goal is to enable rapid iterations of our autonomous software ML stack and optimize our training and deployment processes,” says Nicolas Jourdan, Engineering Manager of the ML Frameworks team. “This work is crucial for accelerating the development of safe, reliable autonomous trucking technology.”
Breaking New Ground
The team’s efforts center around two ML initiatives: the Joint Training Framework (JTF) and the Joint Deployment Framework (JDF). The JTF restructures how ML models are trained, while the JDF transforms how these models are eventually deployed to our autonomous ready Freightliner Cascadia trucks.
Recently, the team reached a significant milestone: automated model optimization and deployment tests on Hardware-in-the-Loop (HIL) benches. Instead of having to request a truck for every deployment test of machine learning components, the teams can run tests on mirroring embedded hardware, which is tightly integrated in the cloud workflows of the team.
This breakthrough allows Torc to test ML models in a production-like environment more efficiently and scalable than ever before.
The Key to L4 Autonomy
The ML Frameworks team’s work is crucial for making Level 4 autonomous trucking a reality on U.S. public roads. “Our frameworks and standards are the backbone that will enable rapid product software releases,” Jourdan emphasizes. “In the fast-paced world of autonomous vehicle development, this ability to iterate quickly and deploy safely is what will set Torc apart.”
A Vision of Transformative Change
Fiete Botschen, Torc’s division lead for the Machine Learning Training and Release Factory, highlights the transformative potential of Machine Learning: “At Torc, we are not just developing autonomous vehicles. We are developing a data driven ecosystem, which allows us to improve our trucking software stack purely by consuming the data our trucks are collecting. This is the key enabler for expanding our logistics network. We will be able to scale our business rapidly once our production trucks hit the road.”
“As part of the Frameworks team, my daily work focuses on building a robust and scalable deployment infrastructure to ensure that every machine learning model operates with the highest reliability in an L4 autonomous environment. By driving seamless integration of complex ML models on embedded hardware, optimized for real-time performance, we are setting new industry standards. This infrastructure is critical for autonomous trucks to navigate dynamic road conditions safely and efficiently, and it reflects the foundational work I do each day to advance Torc’s leadership in autonomous freight.”
Long-Term Impact
The impact of the Torc ML Frameworks team is forward thinking. As autonomous vehicles become more prevalent, the robust, scalable systems developed by this team will be essential for:
- Rapid adaptation to new road conditions and scenarios
- Seamless integration of advancements in AI and machine learning
- Scaling our compute needs with a strong, cloud-based backend
- Monitoring and securing data standards
“In essence, we’re building the brain that will power the Torc autonomous trucking software,” Jourdan explains. “Our work today will enable more efficient logistics, and a robust transportation industry “
Spotlight on Innovation
Torc’s strength is its people. The ML Frameworks team is driven by the collective efforts of talented individuals working together to push the boundaries of what’s possible. The Joint Training Framework and Joint Deployment Framework is the groundwork for an adaptable future for autonomous technology.
Key contributors like Achyut Boggaram have been instrumental in designing and implementing crucial components such as Unified Data Loading Pipelines and Joint Deployment Framework. This technology enhances our ability to process complex sensor data and streamline our model deployment process, significantly reducing the time from development to real-world testing.
The team’s contributions extend beyond technical development. They’ve built a collaborative community spanning multiple divisions within Torc, fostering knowledge sharing and driving innovation. Their mentorship and proactive approach to problem-solving have been invaluable.
Botschen emphasizes, “The dedication and innovation shown by our ML Frameworks team is what makes our ambitious goals achievable. Their ability to solve complex problems, collaborate across teams, and continuously push the boundaries of what’s possible is what sets Torc apart in this competitive field.”
At Torc, we’re proud of the groundbreaking work our ML Frameworks team is doing. As we continue to drive the future of freight, we’re driven by a vision of safe, more efficient transport, Stay tuned for more updates as we continue our journey toward bringing L4 autonomous trucks to market.
