Abstract- Digital Mock-Up (DMU) is a vital process in the automotive industry, enabling teams to visualize, analyze, and optimize vehicle designs before production. Artificial Intelligence (AI) in the Digital Mock-Up (DMU) processes used in vehicle development. DMU is a virtual representation of a vehicle or component that allows for design, analysis, and simulation without the need for physical prototypes. By introducing artificial intelligence into vehicle DMU, manufacturers can leverage advanced analytics, automation, and predictive modelling to streamline workflows and enhance design accuracy. AI in digital mock-up helps optimizing space, resulting in smaller car design. This explores how AI-driven approaches are transforming DMU processes, improving efficiency, and shaping the future of automotive innovation. Key Benefits of AI in Vehicle DMU: - Enhanced Design Analysis: AI-enhanced design analysis enables teams to rapidly assess complex automotive models, detecting issues early and proposing solutions automatically. By automating tedious review tasks, AI ensures a faster, more thorough design validation process, increasing precision and reducing the risk of costly rework in later stages. - Real-Time Feedback: With AI integration, engineers receive real-time feedback as they modify DMU models. Immediate insights into potential weaknesses or opportunities for optimization faster quicker iterative development, empowering teams to achieve superior performance and innovation in each design cycle. - Cross-Team Collaboration: By leveraging AI-powered collaboration platforms, engineers and designers across global teams can seamlessly share updates, suggestions, and design improvements. This fosters enhanced teamwork, breaks down communication barriers, and accelerates the path from idea to prototype in the vehicle development process. - Faster Iterations: AI automates analysis, accelerating design cycles significantly. - Error Detection and Quality Analysis: a) Automated Interference Checks: AI detects part collisions or interferences in complex assemblies. b) Anomaly Detection: Using machine learning, systems can predict potential design flaws or tolerance issues. - Human-Machine Interaction and Ergonomics: AI-driven tools can assess human factors like reachability, visibility, and comfort using virtual human avatars and real-time feedback. - Natural Language Processing (NLP): AI assistants integrated into DMU tools can respond to engineers' questions about the model, speeding up navigation and issue tracking. - Data Management and Decision Support: AI helps manage vast amounts of design and simulation data by identifying patterns, predicting outcomes, and recommending changes. Intelligent versioning and traceability in PLM systems. - VR/AR and AI Integration: When DMU is combined with AI and AR/VR, designers and engineers can interact with the vehicle mock-up in an immersive environment, and AI can guide or assist in real-time. - Error Reduction: AI identifies clashes and inefficiencies in design early in design. Packaging Optimization Steps using AI: • AI analyze CAD data, identifying potential clashes quickly. It can simulate different layouts and suggest better placements of objects/components. • Algorithms optimize for various criteria like weight, cost and safety. • AI balances attributes to get the overall best design package. • AI enhances packaging leading to more efficiently and better performing vehicles. • All these can be done faster. Benefits of AI in Vehicle DMU • Faster Design Iteration: AI-powered collaboration platforms break down silos between design, engineering, and manufacturing teams, creating an environment for faster idea exchange and rapid problem-solving. Teams can iterate on designs with real-time feedback, shortening development cycles and enhancing innovation. Automated documentation ensures that changes and decisions are precisely tracked, enabling seamless knowledge transfer and regulatory compliance throughout the vehicle DMU process. • Better Cross-Domain Integration: AI tools facilitate rapid design evolution by automating repetitive review tasks and delivering instant feedback, allowing teams to quickly test and refine multiple concepts without delays. • Improved Documentation: Seamless sharing of data and insights across disciplines enables mechanical, electrical, and software specialists to collaborate easier, aligning goals and minimizing integration errors. • Faster Product Development: AI accelerates design iterations by automatically detecting design flaws and suggesting optimizations. Reduces reliance on physical prototypes, saving weeks or months in development time. • Improved Design Quality: AI detects interference, tolerance mismatches, and ergonomic issues early in the design process. Ensures more accurate and validated 3D models before any real-world testing. • Cost Reduction: Reduces the number of physical prototypes needed. Avoids late-stage changes by catching issues earlier, significantly lowering development costs. • Better Decision Making: AI provides data-driven insights to engineers and designers. Helps in selecting optimal design choices, materials, or configurations based on performance, cost, and manufacturability. • Predictive Maintenance & Simulation: AI simulates real-world usage and predicts component wear, failure, or service needs before they occur. Enables predictive engineering instead of reactive fixes. • Enhanced User Experience Testing: Virtual testing of human interaction (e.g., comfort, visibility, accessibility) helps improve driver and passenger experience early in the design. • Sustainability: AI helps optimize material usage, reduce waste, and improve energy efficiency of vehicle components. Enables life cycle analysis within DMU models to guide environmentally friendly choices. • Scalability: Once trained, AI models can be reused across multiple vehicle programs, platforms, or components, increasing efficiency and consistency.