In the first half of 2026, around 165 brand-new vehicle models hit the Chinese market, meaning more than one new car was launched on an average working day. Meanwhile, some automakers have shortened new vehicle development cycles from over 36 months in the traditional ICE era to roughly 18 months.

Faster development boosts efficiency, yet it has sparked debates over "rush-built vehicles". When validation procedures are compressed, will vehicles lose safety redundancy? Can AI truly empower R&D and validation, instead of merely serving as an enabler for hasty development? This is not only a technical issue but also an industrial governance challenge.

AI Redefines the Efficiency Boundary of R&D and Validation

In automotive research and development, AI has moved beyond conceptual trials and seen large-scale deployment.

Under joint AI solutions built by automakers and tech firms, perception models can run simulation tests for over 500 scenarios daily. End-to-end model validation cuts the number of prototype vehicles by 10%. Within a virtual digital test site developed by an automotive R&D institute, more than one million kilometers of simulated driving tests are completed each day. Extreme conditions such as heavy rain, backlight glare and "ghost probe" hazards can be reproduced anytime, with hundreds of industrial agents embedded across R&D, simulation and testing workflows.

Beyond OEMs, suppliers have deployed fully automatic automotive electronic testing systems for intelligent driving and cabin products, lifting testing efficiency by 60% and fault diagnosis efficiency by 80%.

These figures point to a clear trend: AI is taking over massive repetitive, inefficient trial-and-error work in R&D. Engineers can therefore devote more energy to sophisticated engineering judgment, system integration and innovative development.

At the 4th AI-Defined Automobile Forum, Yang Qingfeng, Chief AI Architect of IAV, noted that the core logic of AI empowerment lies in the transition "from engineering experience to engineering intelligence". Engineering know-how is converted into reusable skills, and task orchestration enables automatic execution across test benches, vehicles and testing equipment, forming a closed loop from task preparation to acceptance. According to him, Agents can connect engineering tasks with professional software in modeling and simulation. AI can also participate in test case generation, requirement parsing and defect management.

Nevertheless, most of these achievements are based on enterprise-specific cases. The industry lacks unified benchmarks and third-party verification mechanisms to evaluate the performance of AI R&D tools. Fragmented modeling remains a prominent challenge. Vehicle development involves multi-disciplinary and multi-physics models, while different sub-systems are developed by separate teams using diverse tools. Inconsistent data standards create "model silos", limiting the full potential of AI in complete vehicle development.

Validation Tasks That AI Cannot Replace

Rapid advances in AI simulation fuel the temptation to replace physical road tests entirely with digital simulations.

However, the industry has reached a relatively clear consensus on the limits of this substitution. Some automakers rely heavily on simulation data to complete component lifespan validation and drastically reduce physical testing ratios. Simulations excel at modeling repeatable scenarios, yet material aging, wear and random failures in the physical world accumulate over time and cannot be fully replicated by digital models.

The technical limitations of simulation are structural. Errors exist in simulated sensor signals; point clouds from LiDAR and millimeter-wave radar lack sufficient simulation accuracy under complex operating conditions. Road regulations and driving habits vary widely across regions, making it impossible to cover all geographically specific long-tail scenarios via simulation.

A deeper challenge is the reality-simulation gap. Composite working conditions formed by overlapping real-world factors require physical vehicle tests for final reliability verification. Multiple automaker executives emphasized at the 2026 Chengdu Auto Show: "Metal fatigue takes time, rubber aging takes time, battery charge-discharge cycling takes time, and full-vehicle durability also takes time. None of this time can be cut short or substituted by AI simulation."

More importantly, no universal standard defines the safety boundary between "AI-assisted validation" and "validation fully dependent on AI". Recent regulatory actions mainly focus on setting minimum validation requirements rather than defining AI boundaries.

In August 2026, the National Technical Committee of Auto Standardization issued three vehicle type approval test procedures for public comments, proposing to unify the minimum reliability test mileage to no less than 30,000 km, aligning with standards for internal combustion engine vehicles.

In the same month, MIIT together with three other authorities launched a one-year special campaign targeting production consistency and quality improvement of road vehicles. It centers on four key areas: production consistency, reliability, durability and testing validation of new technologies. Cui Dongshu, Secretary General of CPCA, commented that this realizes equal validation standards for ICE and new-energy vehicles. Unified test mileage helps fully expose long-term aging issues of the three-electric system and chassis.

Yang Qingfeng echoed this view. He stressed that standards grow even more critical after AI is introduced. While AI functions as a tool, it carries risks of hallucination. How to constrain AI with standards and guarantee reliable outputs is a key question for industrial adoption. This aligns with regulators’ push for stricter validation standards: AI can accelerate R&D only on the premise of standardized workflows and rigid criteria. AI should help validation run faster and cover more scenarios under existing standards, rather than replace validation itself.

Conclusion

AI-enabled automotive R&D and validation is essentially an efficiency revolution. It frees engineers from heavy repetitive labor and makes large-scale simulation, intelligent test case generation and automated validation closed loops feasible.

AI speeds up development cycles, yet it cannot replicate the gradual build-up of metal fatigue or unmodeled random incidents on real roads. Striking a balance between speed and stability calls for neither over-reliance on AI nor conservative resistance to technological progress. What matters most is respect for the science of validation.