DeepRoute.ai CEO Maxwell Zhou: Aiming to Become the AI Infrastructure of the Physical World
BEIJING, April 29, 2026 /PRNewswire/ -- At the 19th Beijing International Automotive Exhibition (hereinafter referred to as "Auto China"), DeepRoute.ai held a press conference to showcase its latest advances in Physical AI. During the event, CEO Maxwell Zhou reflected on the company's founding mission and outlined its latest advances and vision in Physical AI. Chief Scientist Chong Ruan then delivered his first public keynote, providing a systematic overview of the company's technical architecture around its Foundation Model. The event marks a milestone in DeepRoute.ai's push to establish leadership in Physical AI and shape the direction of next-generation advanced intelligent driving systems.
Maxwell Zhou: Aiming to Become the AI Infrastructure of the Physical World
Opening the press conference, CEO Maxwell Zhou recounted a traffic accident that occurred near him in the early days of his startup journey in 2016. "At that time, I wondered whether we could use AI technology to save more lives," Zhou said. He acknowledged that current advanced intelligent driving systems are not yet perfect, with MPCI (Miles Per Critical Intervention) in urban areas still measured in the tens of kilometers, but noted that available data indicates their safety is already several times higher than that of human drivers. "We believe that within the next two to three years, as large models continue to develop their comprehension capabilities, we will achieve truly safe advanced intelligent driving systems."
Zhou set out a long-term vision for DeepRoute.ai: "I hope that in the future, the company will become the AI infrastructure of the physical world, serving as a foundational capability that sustains real-world operations, much like telecommunications and electricity. When people talk about intelligence in the physical world, DeepRoute.ai should be an essential part of that foundation."
Chief Scientist Chong Ruan's Keynote: Updates on the Foundation Model
Chong Ruan, former Head of R&D at DeepSeek and a core researcher in multimodal AI, made his public debut as DeepRoute.ai's Chief Scientist at this event. He provided a systematic overview of the Foundation Model and the latest progress in building cognitive capabilities for the advanced intelligent driving system.
Ruan noted that as intelligent driving enters the mass production phase, earlier approaches relying on smaller models have shown limited progress in system stability and consistent user adoption. These systems still exhibit performance fluctuations in complex, edge-case scenarios, and a reliable foundation of trust in the driving experience has yet to be established. To address this, DeepRoute.ai has developed a next-generation technical approach centred on the Foundation Model.
The Foundation Model unifies driving decision-making, scene understanding, and behaviour evaluation within a single architecture. By leveraging greater model scale, higher data quality, and a faster data-driven closed-loop, it enables the continuous improvement of the advanced intelligent driving system. Under this framework, the iteration cycle of the data-driven closed-loop has been cut from approximately five days to around 12 hours, significantly improving operational efficiency.
Ruan also noted that the value of the Foundation Model extends beyond product capabilities and is now influencing how the organisation operates. "From internal knowledge base Q&A and automated code generation to cross-departmental collaboration and autonomous experimental analysis, AI is reshaping our R&D and management workflows."
Cross-Industry Dialogue: Focusing on the Core Proposition of "AI for what"
At the press conference, DeepRoute.ai also hosted an "AI Talk" industry dialogue themed "AI for what." The panel was moderated by Li Zhang, Professor at the School of Data Science at Fudan University. Participants included Jian Huo, General Manager of Automotive and Energy Solutions at Alibaba Cloud; Yinghao Xu, Assistant Professor at HKUST CSE and Staff Research Scientist at RobbyAnt; Hao Jingfang, Hugo Award-winning author, Founder of Tong Xing College, and holder of a PhD in Economics and an M.S. in Astrophysics from Tsinghua University; and Chong Ruan.
Unlike traditional product presentations, the dialogue was structured around a series of probing questions: from the capability boundaries of large models in real-world environments and the debate between World Models and VLA models, to the broader societal impact of Physical AI. Each question built on the last, keeping the discussion focused on the fundamental question of what AI is ultimately for.
Propelled by the Data Flywheel for Scaled Evolution, Fully Entering the Era of Physical AI
During the event, DeepRoute.ai also previewed its Cabin-Driving Integration Agent. Rather than functioning as a conventional voice assistant or in-vehicle infotainment system, the feature is designed to evolve the system into an "AI Brain" capable of understanding user needs and responding proactively to complex scenarios.
DeepRoute.ai reports that mass production vehicles equipped with its Urban NOA solution have now exceeded 300,000 units. Over the past year, vehicles running DeepRoute.ai's active safety systems have accumulated over 1.3 billion kilometres of real-world road operation and 44.8 million hours of user driving time. This volume of real-world data, generated through the Data Flywheel, both validates the system's safety performance and provides a critical foundation for the ongoing optimisation of the Foundation Model.
By 2026, DeepRoute.ai plans to grow mass production delivery of its advanced intelligent driving system past one million units. The company also aims to increase its MPCI metric to over 1,000 kilometres and raise its active daily use rate to over 50%. These targets are intended to drive continued improvements in system safety, stability, and user experience, advancing the commercial deployment of Physical AI at scale.
SOURCE DeepRoute.ai
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