Autonomous trucking companies have long faced a costly bottleneck: switching an autonomous driving system from one truck platform to another typically requires a year or more of engineering work, new data collection, retraining, and validation. Waabi, a self-driving technology company, recently claimed it has eliminated that process entirely.
Waabi announced in a blog post that its Waabi Driver software, trained exclusively on a Peterbilt 579, took control of a Volvo VNL Autonomous truck and drove it safely on highways and complex surface streets from the very first mile. According to Waabi, no new real-world data, simulation data, fine-tuning, or engineering work was required.
“This was a massive announcement, Thomas. It was massive for Waabi, but for the industry and Physical AI in general,” Waabi founder and CEO Raquel Urtasun said in an interview with FreightWaves.
The test, conducted with partner Volvo Autonomous Solutions, involved two trucks with significantly different configurations. Urtasun noted that sensor placement, vehicle shape, and control systems all vary between the Peterbilt 579 and the Volvo VNL Autonomous. “The sensors are in very different locations. The shape of the truck is very different. The way you control the Volvo VNL is also very different, and it feels very different from driving a Peterbilt,” she said. “Yet we required zero changes. It was directly plug-and-play. Same stack. Same model. Same everything.”
The Volvo VNL Autonomous successfully performed lane changes, traffic light navigation, right turns, three-way intersections, and U-turns on its first outing with the Waabi software, according to the company.
| Manuever | Result |
|---|---|
| Lane changes | Handled on first try |
| Traffic lights | Navigated safely |
| Right turns | Completed successfully |
| Three-way intersections | Negotiated without issue |
| U-turns | Executed on first outing |
Historically, such platform switches have been one of the most difficult challenges in autonomous driving. “Up to this point, whenever anyone in the industry wanted to move from one vehicle platform to another, it typically required more than a year of engineering work,” Urtasun said. “‘Quickly’ was actually zero. The system generalized without needing anything. You really break the physics of what everyone believed was possible.”
Nils Jaeger, president of Volvo Autonomous Solutions, called the road test “an important proof point of our partnership with Waabi” in a statement. “It also demonstrates the scalability of Volvo’s autonomous truck platform, which is designed to integrate different vehicle models and virtual drivers to enable a wide range of use cases and applications,” Jaeger said. “Together with Waabi, we are advancing autonomous transport solutions toward commercial reality.”
Why Platform Generalization Matters for Scaling
For carriers and OEMs, the practical upside is speed of deployment. Waabi describes two kinds of generalization that matter for scaling: across environments—meaning from highways to dense urban streets—and across embodiments—meaning entirely different vehicles. The Volvo test proved the second.
Urtasun said the capability extends beyond trucking to other vehicle classes and to sensor hardware itself. “The same applies across different vehicle classes. Today we do Class 8 trucks. Maybe tomorrow you want a solution that can do Class 5, Class 6, robotaxis, whatever it is,” she said. “If a new sensor comes to market that’s significantly more capable or much cheaper, you want to be able to take advantage of it immediately.”
AV providers have told FreightWaves that switching sensor suites is a persistent pain point, forcing them to retrain their systems repeatedly as new hardware arrives. Waabi argues that dependency disappears once a single model generalizes across platforms and sensor suites.
Technical Approach: Reasoning Over Raw Compute
Urtasun credited the result to Waabi’s underlying architecture, which she contrasted with rivals betting on scale alone. “If you look at previous generations of AI technology, version 1.0 was built around handcrafted programming. Those systems don’t generalize at all,” she said. “Then came this black-box architecture where the answer became more data, more chips, bigger data centers. You see some of the players investing billions.” As the source text cuts off, the implication is that Waabi’s approach differs by emphasizing reasoning over brute-force compute.
The demonstration marks a significant step toward reducing the engineering overhead required to deploy autonomous trucks across different platforms, which could accelerate the commercial viability of self-driving freight operations.
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