Artificial intelligence (AI) is an evolving technology that is still growing, but it is undoubtedly getting better. 

For all we know, factories might not even need lights 20 years later, because most of them will be operated by AI. We see a lot of R&D happening within the AI framework, giving good results. Besides, we see newer frameworks coming in. 

AI-based visual inspection, too, has been growing by leaps and bounds, reshaping automotive inspection. It helps examine detailed defects in vehicles, providing automotive OEMs the opportunity for accuracy and cost-savings. 

One such company focusing on AI-based visual inspection is Lincode Labs, whose AI-backed visual inspection solution, Lincode Visual Inspection System (LIVIS), is its current focus. The company was started with complete research and understanding of the top challenges that manufacturers globally face. After interviewing close to 100 customers, 86 percent of them said that quality inspection happens to be their biggest challenge.

“We were intrigued by this and went to various quality inspection people and identified the technology they were using,” says Rajesh Iyengar, Founder and CEO, Lincode Labs, and goes on, “We went on to find out that the technology hasn’t changed for two decades and there were a lot of false calls in it. That’s when Lincode stepped in and built a product around specific challenges focused on the automotive industry.”

Automotive OEMs, too, look at specifically reducing these false calls and improving productivity, which Lincode has helped solve through its AI-backed visual inspection solution. “The industry standards were 150 to 200 false calls per million inspections. So, in our case, we are doing it in zero to four parts per million,” Iyengar cites.  

Iyengar further reveals that due to this, 80 percent of their customers are repeat orders. “That’s because they are completely happy with the inspection process and the way the inspection is automated,” he mentions.  

LIVIS
Traditional vision systems cannot catch up with AI, as Iyengar says. He avers, “LIVIS stands out because we have built it as a platform. The scalability becomes easier if you’re going to deploy it across multiple factories and locations. But also, the foremost important thing is that it is completely made as a product. Thus, AI is commoditised. With the LIVIS platform, we can bring the cost and time to deployment down.” 

Lincode’s role in the automotive industry
What’s interesting is that even if Lincode caters to the manufacturing industry as a whole, it first addressed the automotive industry. The company researched the market size of various manufacturing companies and the automotive industry took the top spot, with close to USD 542 billion of global value.  

“We started with the automotive industry but pivoted later,” Iyengar tells us and continues, “So, instead of looking at just the automotive or any other industry, we turned our attention to steel, metal, plastic, glass etc. We basically went to the surface and saw how steel and metal are produced today. Whether it’s a CNC machining or forging or casting process, these are major processes used for any industry across the globe involving steel and metal. We understood that steel and metal are dealt with in the same way globally. Therefore, it made sense to go to the surface and into these kinds of defects specifically, and then generalise that and start building a model towards it. This, plus making AI as a product, has made deployment easier across the globe.” 

R&D centre in Bengaluru
Lincode recently opened a new R&D centre in Bengaluru, which also has a significant role to play in deploying the company’s solution across the world.

Stressing on the fact that evolving models are important in AI, Iyengar states, “It’s a continuous process; it’s not that you just build a model and you’re set. We have a big roadmap in the product development, and the Bengaluru R&D centre is going to play a major role in that. We are going to conduct deep research with various data collected across the globe and do various testing with that.” 

Staying ahead
What’s more, Lincode recently closed a funding round in December last year. Catering to a constantly evolving industry like the automotive, Lincode, too, strives to make sure that its visual inspection solution stays ahead and is put to use. “There are about more than 600 parts in a car and each part is segregated – like the structure, wiring, engine components etc.,” Iyengar shares and continues, “These segregations are made so that we can target the sector of the product. For example, when it comes to engine blocks, there is a specific model with a huge data set around engine blocks. This is how we stay ahead of competition.” Iyengar also adds that their trials with various use cases made them understand that inspection alone is not important but also the way the inspection is done. 

Essential skill sets for AI vision systems
Leveraging AI-based visual inspection solutions in the automotive industry is bound to increase productivity, and the cost of labour will also come down because of automation. “Today, most manufacturers use secondary inspection, which can be cut off straight away. This will improve their productivity and also reduce the risk of delays,” Iyengar enlightens. 

Moreover, AI vision systems come with their share of essential skill sets to bring out the best in the automotive industry. Iyengar states that, in general, skilling is required for the factory people. “This could be at various levels,” he puts across and adds, “It could be for the operators, the IT administrator or even the software development team. Hence, deep training is required, which can be somewhat cumbersome because it could be a bit challenging for the operator. So, an IT person might be needed in order to help the operator every time there is a downtime.” 

Covid-19 and AI-backed visual inspection
Such training or skills could certainly come in handy, because Iyengar claims that the need for AI-backed visual inspection solutions in the automotive industry has increased since the Covid-19 pandemic. “Unplanned shutdowns happened during Covid, because of which employees could not report and manufacturing could not continue properly,” he responds and adds, “Hence, a lot of investments are happening because of this. In fact, even now, a lot of employees are still not reporting and the labour problem has become global. It has become tough to get skilled workers. This has led to the adoption of autonomous manufacturing for automotives, where AI is going to play a big role.”

Meeting industry requirements
For an industry that is an economic force globally, AI-based visual inspection is certainly meeting the high-quality requirements of the customers of the automotive sector. Plus, we already see companies like Volvo using the technology. Safety surpasses any requirement, and this requirement can be fulfilled if quality is top-notch. And quality will be at its best if automotive manufacturers can perform production quality inspections in the most efficient way. (MT)

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Wayve, NVIDIA and Stellantis have announced a joint collaboration alongside the French government at the Assises du Véhicule Autonome to support the deployment of autonomous vehicles in France and across the European Union.

The initiative combines Stellantis’ L4-Ready Platforms with the Wayve AI Driver system, integrating NVIDIA Halos OS on the NVIDIA Hyperion compute and sensor architecture. The collaboration aims to establish an autonomous vehicle value chain in France, spanning silicon development, software integration and vehicle manufacturing.

Local transport partners will engage with trade unions, municipal authorities, and mobility stakeholders to address regulatory alignment, safety requirements, and operational deployment.

Philippe Tabarot, French Minister of Transports, said, "I am pleased to see this collaboration between Wayve, NVIDIA and Stellantis launched at the Autonomous Vehicle Forum in France. France is committed to adapting its regulatory framework to accelerate the development of autonomous vehicles while maintaining strict safety and data sovereignty requirements. By bringing together trade unions, city authorities, and mobility stakeholders, this collaboration embodies the collaborative spirit of the Assises du Véhicule Autonome, as well as the approach required to transition from trials to real-world deployment."

Kaity Fischer, VP Autonomous Mobility & Operations, Wayve, said, "France is leading the way in preparing for autonomous vehicles, and we’re proud to enter this collaboration at such an important moment. By working directly with the French Government and our partners, we can help shape how this technology is deployed safely and responsibly and accelerate its development across the country and entire EU."

Anne Laliron, SVP of Technology Research, Stellantis, said, "This collaboration will help accelerate the delivery of autonomous driving technologies that make mobility safer and more convenient for customers. While its impact is global, France is one of Stellantis’ key centres for engineering and technology development, making it a natural home for advancing the future of AI-powered mobility."

Ali Kani, VP of Automotive, NVIDIA, said, "Bringing safe autonomous mobility to Europe requires deep collaboration across AI, computing and vehicle engineering. NVIDIA Hyperion platform, combined with Wayve’s embodied AI and Stellantis’ automotive expertise, provides a foundation for that ambition. Together with the French Government and local partners, we can help build an ecosystem that brings the benefits of autonomous driving to communities across France and Europe."

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German luxury carmaker Audi has introduced third-generation digital OLED rear lighting technology on the Q91, marking the production debut of three-dimensionally curved digital OLED panels in the automotive industry.

The lighting system incorporates carrier glass measuring 0.1 millimetres in thickness, allowing a bend radius of up to 200 millimetres to match the vehicle's body contours. The Q91 uses six digital OLED panels housing 512 individual segments, including an outer curved panel with 99 segments.

The curved positioning extends light visibility to wider lateral angles and incorporates warning symbols within the communication light system to alert surrounding traffic to breakdowns, emergency braking or accidents. Up to eight digital light signatures are available across static, active and semi-active display settings.

The vehicle also features digital Matrix LED headlights utilising a 13-millimetre micro-LED module containing 25,600 individual micro-LEDs. The headlight system projects high-resolution lane lighting, orientation paths and dynamic turn signal graphics directly onto the road surface to assist lane centring and indicate directional changes to pedestrians and other vehicles.

Audi first introduced OLED technology on the TT RS in 2016, followed by segmented digital OLEDs on the Q5 in 2020 and second-generation panels on the Q6 e-tron in 2024.

Stefan Berlitz, Head of Lighting Development at AUDI AG, said, "At Audi, light means safety first and foremost – seeing and being seen. Digitalisation opens up entirely new possibilities for further enhancing road safety. The curved digital OLED technology in the Audi Q91 impressively demonstrates this technology’s potential. It combines design freedom, personalization, and communication with the vehicle’s surroundings at a new level. Our vision is to gradually transform light into an intelligent means of communication that makes interaction between the vehicle and its surroundings even safer and more intuitive."

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European ride-hailing platform Bolt has selected the NVIDIA Alpamayo Stack autonomous driving software to operate its vehicle fleet in Europe, starting with Lucid automobiles.

The deployment commences in Madrid in 2027, with subsequent deployment planned in Germany and France. The initiative supports the integration of at least 25,000 Level 4-ready Lucid vehicles equipped with NVIDIA Hyperion compute and sensor architecture. The program contributes to Bolt's objective of deploying 100,000 autonomous vehicles across Europe and the UNECE region by 2035. The system integrates reasoning artificial intelligence with perception and planning modules alongside NVIDIA Halos safety controls.

Jevgeni Kabanov, President and Head of Autonomous Driving, Bolt, said, “Autonomous driving will change how people move around cities, and Europe should play a major role in how that future is built. Our ambition isn't to bring autonomous driving to just a handful of cities. We want to make autonomous ride-hailing available at scale across Europe, with 100,000 vehicles on our platform by 2035. We're bringing together some of the best technologies in the world to make that possible. Following our first autonomous mobility launch in Luxembourg, our next step with NVIDIA and Lucid is to begin a programme in Madrid in 2027. The Region of Madrid is pioneering a structured framework for commercial autonomous mobility in coordination with national authorities. Germany will also be a priority for what comes next, with cities already setting out clear ambitions for autonomous mobility.”

Rishi Dhall, Vice-President of Automotive, NVIDIA, said, “Robotaxis will scale when every vehicle is built on a common, safety-engineered foundation. NVIDIA Hyperion standardises the compute and sensor architecture across vehicle platforms, while NVIDIA Alpamayo Stack and Halos provide the driving intelligence and safety foundation. Bolt’s European platform, operational experience and work with cities and regulators make it a strong partner to bring autonomous ride-hailing to market at scale in Europe.”

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French tier 1 supplier Valeo has announced plans to develop and industrialise high-performance compute platforms utilising the NVIDIA Hyperion reference architecture and NVIDIA Halos safety system.

The initiative combines Valeo's vehicle integration capabilities with centralised computing architectures to support software-defined mobility systems. The platform utilises the NVIDIA DRIVE AGX Thor architecture to deliver processing performance and redundancy for Level 4 autonomous driving, commercial trucking and robotaxi applications. The development framework enables automakers to integrate features and deploy autonomous technologies across vehicle models.

Marc Vrecko, Chief Executive Officer, Valeo Brain Division, said, "Our work with NVIDIA marks a significant milestone in our mission to make autonomous driving a safe, scalable reality. By seamlessly integrating NVIDIA’s advanced compute power into our next-generation platforms, we are delivering the flexible, centralised intelligence vehicle manufacturers require to redefine the driving experience and meet the evolving demands of tomorrow’s intelligent vehicles."

Rishi Dhall, Vice-President of Automotive, NVIDIA, said, "Valeo’s expertise in high-performance computing, vehicle integration and automotive-scale industrialisation will help automakers adopt NVIDIA Hyperion and NVIDIA Halos at production scale. Together, NVIDIA and Valeo are providing the AI performance, functional safety and scalability needed to accelerate the development of Level 4 autonomous vehicles and robotaxis."

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