YAPE ha diffuso questo post
Interesting article highlighting why AI has become essential across the entire automotive value chain. At e-Novia, we develop robotics and AI-driven solutions to address challenges faced by OEMs. Our applications range from E/E architetture design and software optimization (Huxelerate) and on-board systems for customer safety and comfort (e-Shock ) to software solutions for sensing, perception, and decision-making (YAPE)
🌟 AI in the automotive value chain 🌟 Since the launch of ChatGPT, AI seems to be a hot topic 🔥 in automotive 🚗. Implementations started way earlier, here is a short summary. As always, I share my personal thoughts 💭. 👉 Missing something? Please leave a comment! 👉 There is not just one AI 🤖 for the different use cases. Use cases are based on classes like Generative AI, Machine Learning, Computer Vision, ... 👉 It’s crucial to understand that every use case, like ADAS 👾 or conversational AI 🤖 could be based on a combination of different AI models and classes. 👉 The industry is still figuring out how to monetize AI in-cabin features. The business case in the engineering and production value chain is much more obvious 💰. 🌟 What are some of the success factors: 👉 A mantra that I repeat frequently: Customer pain points first: What are the real customer pain points? Are AI solutions the best way to solve them? 🤷🏼♂️ 👉 There is so much hype and noise around the application of AI. Ensure that you really understand what is in. 👉 Take ownership for applying AI across your business. Handing off to suppliers is not an option, if you want to succeed. Professional services could guide you through the complexity of applying AI. 👉 The roll out of AI might not always smooth, as seen at Google and others. It’s important to communicate well, react and fix fast. 🚗 Let’s quickly discuss the value chain: 🤳 Direct customer interaction: 👉 AI enabled voice interfaces becoming the norm across the industry. NIO, XPENG and others stated early and continuously improve their setups with new partners and models. https://lnkd.in/d4TmyFhy 👉 OEMs like Stellantis, Volkswagen or General Motors have released their implementations. https://lnkd.in/dAc3jhGp 👉 AI & ADAS: Suppliers like Momenta, Mobileye or QCraft support car makers 🚙 across the world to implement AI in ADAS. https://lnkd.in/dvMSbNrM 🚨 Safety and uptime maximization: 👉 These solutions run in the background but could improve the customer experience significantly. 👉 AI is applied to get the best out of the vehicle datasets to prevent downtimes and breakdowns of vehicles 📊. Predictive maintenance solutions are most valuable for fleet customers 🚐🚚. https://lnkd.in/duX5jQ27 👉 AI in driver monitoring: It’s applied to analyze the in-cabin sensor data to trigger adaptive alerts ‼️ or make recommendations. ⚙️ The cost, time and performance efficiency gains of AI are further down in the value chain: 👉 ADAS and AD dev requires a lot of simulation, tool chains for virtual simulation from Applied Intuition, Foretellix and others are applied. 👉 Humanoid robots 🤖 are on the agenda of major OEMs. Triggers are to hedge labor shortage scenarios and to increase productivity. 👉 The production / machine setup are the holy core of automotive value creation, so no surprise that AI tool chains and data analytics are rolled out in a super complex environment.