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.

As #TRA2024 draws to a close, it's time to reflect and share our thoughts after this week in Dublin 🇮🇪🍀 With Andromachi Mourtzouchou and Jaime Sierra Muñoz, we have participated to the largest Transport Conference in Europe: Transport Research Arena (TRA). During this week, we have been presenting our researches on the impact of Automated Delivery Robots on public spaces and in terms of planning efficiency, on the challenges related to electric vehicles usage, on the economic impact of automated vehicles and on car dependence assessment. We also promoted our Linving Lab where we investigate new mobility solutions, such as YAPE, the Automated Delivery Robot developed by e-Novia.We are grateful for the time sharing with our DG JRC colleagues and the ones from the other European Commission DGs. 🇪🇺 Hope to see you soon for more insightful exchanges and updates! #scienceforpolicy #europeancommission