Gasgoo Munich- The 2026 China Automotive Forum Technology Leader Summit convened successfully. Peng Xueming, senior chief engineer at Desay SV, dissected the industry's growing pains as new L3 autonomous driving regulations take effect. His diagnosis: split-architecture systems carry steep compliance costs down the line, while low-cost hardware struggles to sustain long-term OTA updates. Too many players, he argued, prioritize short-term feature launches while overlooking the hidden costs of a vehicle's ten-year lifecycle. Drawing on industry trends, he outlined a strategy centered on a unified central computing platform.
Image Source: 2026 China Automotive Forum
The competitive landscape has shifted fundamentally now that China's mandatory national standards for L3 autonomous driving have taken effect. The new regulations do more than expand testing requirements; they clearly define safety responsibilities across the entire autonomous driving chain, demanding a traceable system spanning R&D, verification, production, and after-sales. Peng predicts that the old model of rushing to market with cheap hardware is unsustainable. The battleground has moved to a platform's long-term ability to iterate. The next five years, he suggests, will be the critical window for companies to cement their standing in high-level autonomous driving.
Currently, a vast number of models rely on a split "big and small brain" architecture. While this approach offers lower upfront costs, it accumulates significant technical debt in compliance and iteration over time. If a vehicle needs to upgrade from L3 to L4 later, companies must reinvest resources across the board—development, verification, and OTA. In contrast, a unified, dual high-compute central brain can share a single software and verification stack, enabling a smooth transition from L3 to L4 and avoiding redundant spending. Simultaneously, the industry is moving beyond the limits of traditional rule-based algorithms to fully embrace an AI-native path, relying on massive computing power to help large models navigate complex road conditions.
Peng highlighted three often-overlooked hidden costs: sunk costs from algorithm iterations, expenses for L3 compliance rectification, and the cost of resetting entire platforms. Automakers, he insisted, must plan for the long haul based on a vehicle's ten-year service life. On the hardware front, AI-native L3 models require a baseline of 2,000 TOPS of computing power, while future L4 systems will demand 6,000 TOPS. That compute capacity must leave ample headroom for OTA updates, multi-sensor fusion, and safety redundancy.
Addressing these industry pain points, Desay SV has unveiled a full-domain, high-compute solution based on a "primary domain performs, secondary domain backs up" philosophy. This approach avoids mindlessly stacking chips, instead delivering a complete chain of capabilities spanning software, hardware, safety, and mass production. Peng called on the industry to break free from low-price cutthroat competition and build a unified, secure industrial ecosystem with a long-term mindset. By leveraging proprietary full-domain intelligent architectures, he argued, domestic smart vehicles can boost their global competitiveness.
