Artificial intelligence (AI) is igniting a sweeping technological revolution across the automotive industry, reshaping its landscape from safety features to production lines.

Following the 2025 Spring Festival, DeepSeek, distinguished by its high performance, low cost, and open-source sharing, stirred up the entire automotive sector. To date, over 20 automakers, including Geely, Lantu, Zeekr, Dongfeng, IM Motors, Great Wall, and Chery, have announced deep integration with the DeepSeek large model to enhance the language interaction capabilities of their vehicles' intelligent cockpits.

However, AI's powerful technical capabilities extend beyond intelligent cockpits. They also play a pivotal role in intelligent driving evolution, production process optimization, and intelligent supply chain management. Simultaneously, they introduce new challenges such as data privacy, algorithm bias, and the definition of human-machine rights and responsibilities.

▍The Evolution of Intelligent Driving

In the realm of virtual testing, AI is developing simulation systems that surpass traditional safety standards. Moritz Neukirchner, Senior Director of Strategic Product Management at global supplier Elektrobit, emphasized that generative AI can not only create personalized experiences but also accelerate the development process. Reinforcement learning technology is particularly crucial for the advancement of ADAS (Advanced Driver Assistance Systems) and autonomous driving.

The application of generative AI and reinforcement learning has reached the stage of industrialization. NVIDIA's simulation software, DRIVE Sim, generates data used to train the deep neural networks that form the perception system of autonomous vehicles. Its joint development with Mercedes-Benz on the "Digital Twin Munich" project replicates urban road characteristics with dynamic weather and lighting changes on a 1:1 scale. It also supports the creation of unique Chinese traffic scenarios, reducing the validation cycle of ADAS systems from 18 months to 6 months.

As a cloud service provider for autonomous driving that integrates generative AI, Huawei has established an "Extreme Weather Digital Laboratory" for Thalys, simulating meteorological conditions like freezing rain and fog banks. It generates complex traffic participant behavior models, including "Chinese-style crossing" group motion prediction, which enhances the algorithm iteration speed by 400% and achieves a daily virtual test mileage of 5 million kilometers.

Currently, AI is breaking through the bottleneck of full-chain validation, spanning from MIL (Model-in-the-Loop) to DIL (Driver-in-the-Loop). A McKinsey report predicts that by 2026, virtual testing will handle 90% of autonomous driving validation workloads, reducing the product launch cycle to one-third of the traditional model. This "digital-first" R&D paradigm is redefining the boundaries of automotive safety technology innovation.

Simultaneously, AI is driving the centralized transformation of vehicle electronic architectures, laying a safe and reliable technical foundation for the software-defined vehicle era. The centralization of electronic architectures is not merely a hardware iteration but also a revolution in verification methods. The fusion of digital twins, AI simulations, and cloud-native test platforms aids automakers in overcoming the challenge of "coexistence of old and new systems."

▍The Invisible Driver of the Manufacturing Revolution

In the physical world, AI is reshaping every facet of automotive manufacturing. Changan Automobile employs 5G+AI vision to reconstruct the final assembly process, achieving full-process automation of glass primer coating, gluing, and assembly through binocular cameras and AI vision detection technology. The robot positioning accuracy can reach ±0.1mm, shortening the assembly cycle by 30% and eliminating the issue of uneven sealant caused by manual operation. Additionally, an online AI quality monitoring network has been established, deploying 73 AI vision inspection stations covering 25 scenarios, such as thermal imaging mold temperature monitoring and bolt tightening torque detection. Through real-time data collection (1 million points/second) and deep learning algorithms, the quality defect detection rate has been elevated to 99.97%.

The Ministry of Industry and Information Technology's 2025 Smart Manufacturing White Paper reveals that automakers leveraging AI to optimize assembly processes have reduced manufacturing costs per model by an average of 18% and shortened the new product introduction cycle to one-third of the traditional model. AI technology is driving change in the automotive assembly field from three angles: process precision, decision-making intelligence, and system flexibility.

Regarding the upgrade of intelligent supply chains, AI technology is also playing a significant role. Dongfeng Motor Corporation has established an intelligent management system for vehicle logistics, enabling full-process management of vehicles from production and warehousing to terminal delivery through barcodes. AI algorithms automatically generate storage location suggestions, ensuring that the same vehicle model and color are stored together, increasing space utilization to 95%, avoiding reversing operations during outbound shipments, and improving outbound efficiency by 40%. Ultimately, storage space utilization is increased by 80%, and overall warehousing costs are reduced by 18%. Dongfeng Motor Corporation's technical approach has been included in the recommended solutions in the "China Automotive Intelligent Manufacturing White Paper."

▍Turbulent Waters in the Deep End of Technology

While AI is driving various changes in the automotive industry, AI integration is not a "panacea." Extensive data collection poses significant risks to privacy and security. Data indicates that connected cars generate 25GB of data per hour, 70% of which involves user privacy. The latest EU regulations stipulate that in-vehicle data processing must comply with the "privacy by design" principle, posing severe challenges to the data architecture of traditional automakers.

Furthermore, algorithm bias can distort safety outcomes, making certain populations vulnerable to attacks. The application of AI also raises questions regarding the definition of human-machine rights and responsibilities for autonomous vehicles. As AI evolves vehicles from mere transportation tools to intelligent terminals, the industry must not only innovate in AI technology applications but also overcome application technology barriers and build an innovative ecosystem with aligned values.

Typesetting | Zheng Li

Source | S&P Global

Image Source | Shotu