Programme time: 13-Oct | 11:00 | 60 min
Room: Infante Room (Catalyst Marketplace)

Moderator: TBC
Speakers
•  Artur Rocha — Centre Coordinator, HumanISE (INESC TEC)

Full description and objectives

As vehicles become increasingly connected, automated, and shared, HMIs are shifting from simple controls to trust-building systems that must adapt to diverse users and shifting autonomy levels. AI is a key enabler not only inside the vehicle, but throughout the design process — automating compliance checks, predicting behaviour, and evaluating cognitive load before physical testing.

This session shows how AI, behavioural/cross-market data, and the automated-driving systems they inform converge in real HMI workflows for CCAM. Through two live, end-user-validated use cases — adaptive transition-of-control interfaces for SAE L3 vehicles, and AI-assisted icon consistency across European markets — RE:LAB and its end-user partner demonstrate measurable impact on safety compliance, development time, and cross-market scalability, followed by a live BANCONE demo and structured discussion.

• Showcase concrete AI-powered tools and methodologies applied to HMI design, specification, and validation in mobility contexts,

• Present two grounded industrial use cases with end-user testimony on measurable process impact

• Discuss the challenges of designing interfaces for mixed autonomy levels, shared vehicle users, and diverse driver profiles,

• Identify gaps between current AI capabilities and the actual needs of HMI practitioners and end-users,

• Foster cross-sector dialogue between automotive OEMs, technology providers, researchers, and regulators.

Predefined discussion questions: 1. How can AI support ergonomic and safety compliance in HMI design at scale, without becoming a black box in safety-critical decisions? 2. What human-factor risks does AI introduce — not just solve — in adaptive vehicle interfaces? 3. How do we design for trust when autonomy levels shift dynamically across mixed-autonomy fleets?

Why you want to join?

This session is positioned within the Application and Demonstration track, showcasing a mature ADR solution delivering measurable impact in mobility, a strategic sector of the Apply AI Strategy, with direct participation from industrial end-users.

The workshop demonstrates the convergence of AI, Data, and Robotics through BANCONE®, an AI-powered SaaS platform developed by RE:LAB for HMI design, specification, and compliance validation. Predictive AI models and cross-market interaction data are connected to automated-driving and control systems, demonstrating how research-grade AI capabilities can translate into industrial deployment. Two end-user-validated case studies will illustrate measurable improvements in compliance time, rework cycles, and cross-market scalability.

The session also addresses Europe’s strategic autonomy by examining HMI validation for automated systems against European and international standards, including SAE J2365, ISO 15008, and Euro NCAP. Strengthening European validation capabilities helps retain technological expertise, IP, and skilled jobs within Europe while supporting globally competitive solutions.

Human-centred and responsible AI will be a horizontal theme, covering transparency, explainability, human oversight, cognitive load, driver attention, and trust in safety-critical environments.

Organised by RE:LAB together with OEM/end-user partners, the workshop will bring together approximately 30–40 participants from mobility, AI, human factors, research, industry, regulation, and policymaking. Cross-sector learning with healthcare, industrial systems, and aviation will further support knowledge exchange and the transfer of approaches across safety-critical domains.

Running agenda