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?

 

This session sits squarely in Application and Demonstration Sessions, showcasing a mature, deployed ADR solution generating measurable impact in mobility, one of the strategic sectors of the Apply AI Strategy, with the active involvement of a real end-user partner, as required for this track.

 

• Cross-domain convergence (AI + Data + Robotics): BANCONE’s predictive AI models and cross-market interaction data are applied directly to the automated-driving and control systems that CCAM HMIs must interface with — the workshop makes this AI–Data–Robotics convergence explicit and demonstrable, rather than theoretical.

• Real-world use and value creation: two end-user-validated case studies show a documented path from research-grade AI capability to industrial deployment, with measurable outcomes on compliance time, rework cycles, and cross-market scalability.

• Strategic European positioning: validating HMI for automated systems against European standards (SAE J2365, ISO 15008, Euro NCAP) is a technological-sovereignty asset — it reduces Europe’s dependency on non-harmonised testing methodologies and keeps HMI validation capability, IP, and jobs within the EU regulatory ecosystem, directly reinforcing ADRF26’s 2026 theme of a resilient, strategically autonomous and globally competitive Europe.

• Human-Centred AI and Responsible Innovation (horizontal theme): the session addresses transparency, explainability, and human oversight in AI-assisted design tools deployed in regulated, safety-critical environments.

 

RE:LAB, the workshop organiser, is an Italian company with over 20 years of specialised experience in HMI design across safety-critical sectors, including automotive, transportation, healthcare, and industrial applications. RE:LAB has developed BANCONE®, an AI-powered SaaS platform for HMI design, specification, and compliance validation, used as the concrete, demonstrable backbone of this workshop rather than a theoretical example.

 

The session is co-organised with end-users/OEMs, who will contribute direct testimony on deployment outcomes — meeting ADRF26’s requirement that real-world sessions be anchored in active end-user participation.

 

The workshop is designed for a cross-disciplinary audience of approximately 30–40 participants, reflecting the multi-stakeholder nature of CCAM. It is intentionally cross-sectoral: the AI and human-factors challenges in automotive HMI are structurally similar to those in medical devices, industrial control panels, and aviation cockpits — cross-pollination between these communities is a deliberate design goal. Speaker line-up will aim for gender-balanced representation across RE:LAB and partner contributors.

 

Primary audience: automotive/mobility stakeholders; researchers in cognitive load, driver attention, and trust calibration; automotive engineers and system architects; AI/data practitioners; HMI and human-factors specialists.

Secondary audience: regulators and standardisation experts; policymakers on CCAM and Apply AI Strategy implementation in transport; mobility service operators (shared mobility, public transport, fleet management).