When the Machine Understands the Human: The Advantages of a Scenario-Defined Approach to Rapid AI Implementation in Mobility

Dr. Wang, the title of your ELIV keynote is "The Great Leap Forward". How would you describe the paradigm shift toward the "scenario-defined" approach you are advocating?

Dr. Angela Wang: The paradigm shift toward scenario-defined approaches is a leap from "hunting for scenarios with ready-made technology" to "forging technology based on scenarios" , and an evolution from "feature stacking" to "experience-driven" . In the past, many innovations followed a technology‑first logic, but too often the technology was powerful yet users perceived little real value. While scenario-defined approach starts with real user needs in daily life, and works backward to determine what combination of technologies is required. This approach brings three advantages. First, the needs are authentic, so technology investments directly address pain points with no wasted compute. Second, it delivers a closed-loop experience, where all vehicle capabilities are orchestrated around a single scenario rather than existing as isolated functional silos. Third, it provides a clear path for evolution, and scenario data continuously feeds back into the models, and building a differentiated moat that competitors cannot easily replicate.
 

Could you illustrate with an example from everyday driving what it means when development is no longer driven by available technology, but rather by the "scenario"?

Dr. Angela Wang: For the navigation product we are developing, the user's underlying need for in-car navigation is not simply to go somewhere, but to get the most fitting experience in a given context. When arriving in an unfamiliar area in the evening, what they truly want isn't just directions to a restaurant, but to find one nearby with available parking, reasonable prices, food that suits their taste, and no waiting in line. Such needs are vague, context‑dependent, and often not clearly articulated by the user themselves. Traditional navigation systems rely on explicit commands and cannot respond to such intent‑level demands. Our new approach is to deconstruct the user's natural‑language expressions into multidimensional intents covering location, preference, context, and available resources, and then dynamically aggregate data from navigation, parking, merchant popularity, and user behavior to build a "need interpreter" rather than a mere "route calculator." The system no longer depends on fixed rules, instead, it filters candidate options in real time, overlays parking information, and incorporates personal taste to quickly deliver an optimal solution. The experience is elevated from task completion to contextual fulfillment, from "humans adapting to machines" to "machines understanding humans."
 

According to your thesis, AI will completely redefine the user experience. What will such an AI-transformed scenario in the vehicle interior look like in three to five years?

Dr. Angela Wang: Over the next three to five years, AI‑enhanced in‑vehicle scenarios will shift from passive response to proactive companionship, including proactive anticipation, seamless services, deep intent understanding with full‑vehicle coordination, occupant state sensing with personalized care, and end‑to‑end journey connectivity with active protection. The cabin will no longer be a silo, and AI will bridge smartphones, homes, and office devices into a unified ecosystem. Technology fades into the background, yet every detail feels thoughtfully tailored. The car becomes more than transport, it's an intelligent space on wheels. Realizing this, however, requires solving cross‑domain integration, closed‑loop data, and trustworthiness to make AI safe, deep, and personal in every journey.
 

In a future where software and AI are omnipresent, how can automakers continue to build differentiating features and genuine competitive advantages at all?

Dr. Angela Wang: When AI capability is no longer a privilege of a few players but a universally accessible infrastructure across the industry, those who can translate that capability into user‑perceptible, reliable, and emotionally resonant experiences through scenario innovation will win users and build true differentiation in the wave of technological democratization. 

• First, shift from "building a vehicle first and then adding AI" to "AI‑defined vehicle" , and let AI participate from the very start of product definition in demand discovery, scenario simulation, and experience design.
• Second, go deep into scenarios, and upgrade from functional fulfillment to emotional resonance. True differentiation lies in building an intelligent agent that can orchestrate an ecosystem and truly understand users and context.
• Third, the long‑term barrier to scenario innovation requires complete system capabilities, including chips, OS, closed‑loop data, and service ecosystems. OEMs must become AI companies, since the competitive logic is shifting.
   

You describe AI as the "central nervous system" of software development. How does this premise change the daily work and the tools of software engineers in the automotive industry?

Dr. Angela Wang: AI is redefining our software development and moves us from "human-defined" to "AI-generated" to manage the explosion of scenarios, from "real-vehicle testing" to "digital twin" simulation to speed up iteration. This transformation is driven by three fundamental restructurings. 

• The first is cognitive restructuring: building an AI middle platform that converts data into a dynamic knowledge graph, shifting from experience‑driven to data‑intelligence‑driven development.
• The second is process restructuring: decoupling processes and closing feedback loops, with humans focusing on value judgment while AI handles pattern recognition, which enables an autonomous cycle from requirements to operations, improving efficiency, quality, and responsiveness.
• The third is value restructuring: redefining team roles and collaboration, moving from “humans lead, machines execute” to “humans decide, AI enables”. As AI takes over repetitive tasks, human engineers are freed to focus on architecture and innovation.
   

Many experts wish closer collaboration across regional markets. What could such a global alliance between Asian software expertise and classic European engineering look like in practice?

Dr. Angela Wang: These cross‑border alliances are becoming deeper, systematically integrating Europe’s engineering heritage and safety standards with Asia’s software agility, AI capabilities, and strength in scenario innovation. Such multi-layered alliances come in diverse forms, such as strategic investments, AI talent sharing, ecosystem resource sharing, and joint development leveraging "China speed" and localized innovation for rapid market response and higher cost efficiency. Some European OEMs also establish R&D centers in China, with Chinese teams taking the lead in developing models for the European market, and Asian software and AI capabilities are being deeply embedded into the global R&D processes of European OEMs.