HMI Engineering for Autonomous Vehicles (Levels 2–4) — handover, trust, explainability, and supervised driving.
About us HMI Engineering for Autonomous Vehicles (Levels 2–4) — handover, trust, explainability, and supervised driving.
HMI engineering for autonomous vehicles (Levels 2–4) is essential for optimizing human-machine interaction in ADS systems with supervised driving, including advanced handover management, user trust, and algorithm explainability. This field integrates technical areas such as adaptive control, multisensory perception, haptic interfaces, and confidence algorithms based on predictive models, applying methodologies such as SIL, HIL, and real-time simulation to validate the safe transition between autonomous and manual modes. In addition, interpretable machine learning tools, ergonomic design influenced by standards such as ISO 9241, and functional certification frameworks in accordance with international standards are utilized.
The laboratory’s capabilities include interoperability testing of LiDAR, radar, and camera sensors, real-time monitoring with advanced data acquisition, and failure analysis in simulated environments, ensuring traceability in accordance with ISO 26262 and applicable international regulations for functional safety in autonomous vehicles. Alignment with standards promotes the development of specialized roles such as ADAS systems engineer, HMI validation specialist, functional safety analyst, embedded software developer, and regulatory compliance auditor, strengthening the sector with multidisciplinary profiles focused on mobility
The laboratory’s capabilities include interoperability testing of LiDAR, radar, and camera sensors; real-time monitoring with advanced data acquisition; and failure analysis in simulated environments, ensuring traceability in accordance with ISO 26262 and applicable international standards for functional safety in autonomous vehicles. Alignment with standards promotes the development of specialized roles such as ADAS systems engineer, HMI validation specialist, functional safety analyst, embedded software developer, and regulatory compliance auditor, strengthening the sector with multidisciplinary profiles focused on automated mobility.
Target keywords (natural in the text): handover, trust, explainability, supervised driving, HMI, ADS, SIL, HIL, ISO 26262, sensory integration, functional safety.
HMI Engineering for Autonomous Vehicles (Levels 2–4) — handover, trust, explainability, and supervised driving.
- Format: Online
- Duration: 19 months
- Time: 1900 H
- Practices: Consult
- Language: ES / EN
- Credits: 60 ECTS
- Registration date: 15-05-2026
- Start date: 09-07-2026
- Available places: 5
392.000 $
Skills and results
What you will learn
1. HMI in Autonomous Vehicles (Levels 2–4): Handover, Trust, Explainability, and Supervised Driving
- Analyze handover between the driver and the autonomous system in L2–L4 environments, optimizing transition times, user interfaces, and cognitive load to increase confidence and explainability of the system’s decisions.
- Design and implement Supervised Driving and HMI interfaces that integrate status monitoring, confidence indicators, and control negotiation strategies for L2–L4 scenarios.
- Implement metrics for explainability and confidence in AI for HMIs, developing interpretable interfaces and interaction flows that allow users to understand and predict the system’s decisions.
2. HMI Optimization in Autonomous Vehicles (Levels 2–4): Handover, Credibility, Clarity, and Supervision
- Analyze handover between humans and systems, the reliability of information, and the clarity of status indicators and alerts.
- Design HMI interfaces that optimize supervision at Levels 2-4, focused on usability, the transparency of decisions, and the reduction of ambiguities.
- Define safety and reliability metrics for the handover and validation of decisions, with testing in operational scenarios and acceptance criteria.
3. Comprehensive user-centered design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process—from concept to final validation—using user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. HMI Design and Management for Autonomous Vehicles (Levels 2–4): Control, Safety, Perception, and Control
- Analyze the transfer of control between the autonomous system and the driver, including handover policies, safety criteria, and transition times in L2–L4 scenarios.
- Design HMI interfaces focused on operational safety, with alert hierarchies, status indicators, and action guidelines for the driver and occupants.
- Evaluate the understanding of the vehicle’s status and tasks by the user through intuitive indicators, usability tests, and measures of cognitive load.
5. HMI for Autonomous Vehicles (Levels 2–4): Handover, Certainty, Justification, and Assisted Steering
- Analyze the process of control handover between the driver and the system in autonomous vehicles (Levels 2–4), with a focus on transition safety, attention monitoring, and certification criteria.
- Design the HMI interface to achieve operational certainty and decision justification for the system, integrated with traceability templates and support for steering assistance.
- Validate and optimize steering assistance through usability testing, failure simulations, and evaluation of performance indicators and regulatory compliance.
6. HMI Engineering in Autonomous Vehicles (Levels 2–4): Delivery, Trust, Explanation, and Assisted Driving
You will learn to integrate the entire product development process—from concept to final validation—using user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
To whom is our:
HMI Engineering for Autonomous Vehicles (Levels 2–4) — handover, trust, explainability, and supervised driving.
- Engineers with degrees in Aerospace Engineering, Mechanical Engineering, Industrial Engineering, Automation Engineering, or related fields.
- Professionals working at OEMs (original equipment manufacturers) of rotary-wing aircraft/eVTOLs, MRO (maintenance, repair, and overhaul) companies, consulting firms, and technology centers.
- Experts in Flight Testing, aircraft certification, avionics, control systems, and flight dynamics who wish to deepen their knowledge.
- Staff from regulatory bodies/authorities and professionals involved in the development of UAM/eVTOL (Urban Air Mobility/Electric Vertical Takeoff and Landing Vehicles), who need skills in regulatory compliance.
**Recommended Requirements:** Basic knowledge of aerodynamics, automatic control, and structures is recommended. A language proficiency level of Spanish/English B2+/C1 is required. Support programs (bridging tracks) are provided for those who need them.
- Standards-driven curriculum: You will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, and ADS-33E-PRF starting from the first module.
- Accredited laboratories (EN ISO/IEC 17025) with rotor test bench, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
- Evidence-based Master’s Thesis: safety case, test plan, compliance dossier, and operational limits.
- Industry-led mentorship: faculty with experience in rotorcraft, tiltrotor, eVTOL/UAM, and flight testing.
- Flexible format (hybrid/online), international cohorts, and support from SEIUM Career Services.
- Ethics and safety: a focus on safety-by-design, cyber-OT, DIH, and compliance as pillars.
1.1 Definition and Scope of HMI in Autonomous Vehicles
1.2 Levels of Autonomy (L2–L4) and Their Implications for HMI Design
1.3 Principles of Usability, Safety, and User Experience in HMI
1.4 HMI Architecture: Components, Information Flows, and Integration with Sensors and Control
1.5 Handover Between Driver and System: Concepts, Criteria, and Warning Signs
1.6 Trust and Credibility in HMI: Indicators of Reliability and Consistency
1.7 Explainability and transparency of system decisions
1.8 Monitoring and cognitive load: roles of the driver and the system
1.9 Safety, privacy, and regulatory compliance in autonomous vehicle HMIs
1.10 Case study: analysis of an L2–L4 scenario and evaluation of the HMI
2.2 HMI Design: Handover and Trust — Fundamentals of handover and trust-building in L2-L4
2.2 Control Transfer: Handover criteria between the system and the driver, and windows of opportunity
2.3 Trust Measurement: Metrics for credibility, predictability, and perceived reliability
2.4 Explainability: Strategies for explaining system actions and decisions during handover
2.5 Human Supervision: Roles, workload, and intervention limits in driving
2.6 Message Clarity: Design of alerts, indicators, and feedback for rapid response
2.7 Information Flow: HMI architecture and data management during control transfer
2.8 Usability evaluation: tests focused on handover experience and user trust
2.9 Safety in control transfer: fault mitigation, fallback modes, and resilience
2.20 Case study: handover scenarios and trust evaluation using a risk matrixb
3.3 Driver-to-system handover: handover protocols, activation conditions, and confidence metrics
3.2 Decision explanation in the HMI: criteria, traceability, and level of detail based on context and user
3.3 Operational supervision: system status monitoring, cognitive load, and alerts
3.4 Fault management and recovery: safe transitions, fallback modes, and recovery messages
3.5 Adaptive explainability: communication of changes and explanations tailored to different user skill levels
3.6 Supervision auditing and traceability: logs, decision review, and compliance with standards
3.7 Control transfer between L2-L4 modes: activation/deactivation criteria and autonomy limits
3.8 Trust signals in the HMI: visual, auditory, and haptic cues to confirm transfers and actions
3.9 HMI training: transfer simulations, explainability exercises, and supervision drills
3.30 Clinical case: go/no-go with risk matrix: assessment, decision, and mitigation actions
4.4 Transfer of Control: Handover Criteria, Transition Signals, and Conditions for Switching from Autonomous to Supervised Mode in Levels 2–4
4.2 HMI Safety: Strategies to Prevent Transfer Errors, Safe Modes, and Confidence Limits
4.3 User Understanding: Message Clarity, Iconography, and Explainability of System Actions
4.4 Design of Supervision Interfaces: Dashboard Layout, Visual Consistency, and Performance Indicators
4.5 HMI Reliability: Redundancies, robustness testing, and decision logging/traceability
4.6 Supervision and Assisted Driving: Tools for human intervention and intervention criteria in failure scenarios
4.7 Context Transfer Between Autonomy Levels (L2-L4): Preservation of relevant information and operational limits during handover
4.8 AI credibility and explainability: confidence metrics, algorithm transparency, and decision justification
4.9 Access and control security: authentication, role management, permissions, and change control
4.40 Case clinic: go/no-go with a risk matrix for HMI design decisions in L2-L4
5.5 Handover in Autonomous HMIs: Concepts and Challenges
5.5 Introduction to Autonomy Levels L5–L4
5.3 Fundamentals of the Human-Machine Interface (HMI)
5.4 The Role of the HMI in Safety and User Experience
5.5 Key HMI Technologies for Autonomous Vehicles
5.5 Handover Design: Smooth and Safe Transitions
5.5 Building Trust: Visual Elements and Sensory Feedback
5.3 HMI Interaction: User-Centered Design Principles
5.4 Interface Design: Ergonomic and Accessibility Considerations
5.5 Handover and Trust: Case Studies and Best Practices
3.5 Explainability: Communicating System Decisions
3.5 Design of Explainable Interfaces: Indicators and Alerts
3.3 User Control: Driving Modes and Settings
3.4 Explainability: Importance in User Decision-Making
3.5 Tools and Techniques for Creating Explainable HMIs
4.5 Supervision at Autonomy Levels L5–L4
4.5 The Role of Assisted Driving: Capabilities and Limitations
4.3 Interface Design for Effective Supervision
4.4 Integration of Advanced Driver Assistance Systems (ADAS)
4.5 Risk Assessment and Mitigation Strategies
5.5 Handover: Designing for a Smooth Transition Between Modes
5.5 Creating Certainty: Clarity in Displayed Information
5.3 Interface Design: Key Elements for Certainty
5.4 The Role of the HMI in Responding to Unexpected Situations
5.5 HMI Testing and Validation for Handover and Certainty
6.5 HMI Engineering: Processes and Methodologies
6.5 Handover: Designing for Safe and Efficient Control Handover
6.3 Trust: Implementing Feedback and Communication
6.4 HMI Software and Hardware Development
6.5 HMI Testing and Validation
7.5 Advanced Handover: Anticipation and Predictive Communication
7.5 Trust: Emerging Technologies and Their Impact
7.3 Customizable and Adaptive Interfaces
7.4 HMI Design for Different User Profiles
7.5 Case Studies: Trends in Advanced HMI
8.5 Reliability: Designing Robust and Redundant Systems
8.5 Explanation: Communicating Failures and Limitations
8.3 Safety Considerations in HMI Design
8.4 Integration of Sensors and Data into the HMI
8.5 HMI Development Based on Reliability and Explanation
6.6 Handover: Fundamentals and best practices in control transfer.
6.2 Trust: Building user trust in autonomous systems.
6.3 Assisted Driving: Assistance systems and their integration into the HMI.
6.4 Interface Design: User-centered design principles for effective handover.
6.5 Safety: Safety considerations in the user interface for autonomous vehicles.
6.6 Explainability: Designing explainable systems to improve trust.
6.7 Handover in Complex Scenarios: Adapting the Interface to Different Levels of Autonomy.
6.8 Validation and Testing: Validation Methods for HMI Systems.
6.9 Handover and Human Factors: Human Factors Considerations in Handover.
6.60 Future of HMI: Emerging Trends in User Interfaces for Autonomous Vehicles.
7.7 Introduction to HMI in Autonomous Vehicles (Levels 2–4)
7.2 Evolution and Trends in HMI for Autonomous Vehicles
7.3 Regulatory Framework and Industry Standards
7.4 Roles and Responsibilities of the Driver and the System
7.7 General Architecture of the Autonomous HMI
2.7 Handover Design: Principles and Best Practices
2.2 Building Trust in the HMI System
2.3 User-Centered Design and Usability Testing
2.4 Designing Intuitive and Effective Interfaces
2.7 Design Considerations for Different Driving Scenarios
3.7 Explainability in HMI: Concepts and Applications
3.2 Methods for Improving System Understanding
3.3 Design of Control and Feedback Elements
3.4 Visualization of Complex Data
3.7 Implementation of Warning and Alert Systems
4.7 System Monitoring and Assisted Driving Management
4.2 Strategies for Safe Transition Between Driving Modes
4.3 Design of Interfaces for Driving in Complex Environments
4.4 Techniques for Error and Failure Detection
4.7 Legal and Ethical Considerations in Monitoring
7.7 Control Transfer: Design of Scenarios and Protocols
7.2 The Importance of Certainty in Human-Machine Interaction
7.3 Interface Design for Different Levels of Autonomy
7.4 Evaluation and Measurement of Design Effectiveness
7.7 Case Studies and Industry Examples
6.7 Delivery Engineering: Implementation of HMI Systems
6.2 Methods for Building Trust in the HMI
6.3 HMI Software Development
6.4 HMI Validation and Verification
6.7 Integration of the HMI with Other Vehicle Systems
7.7 Advanced Handover: Techniques and Technologies
7.2 System Confidence: Measurement and Improvement Methods
7.3 Design of Adaptive Interfaces
7.4 Real-Time Information Management
7.7 Future Trends in HMI for Autonomous Vehicles
8.7 HMI Reliability: Design and Development
8.2 Methods for Explaining System Decisions
8.3 Design of Driver Monitoring and Assistance Systems
8.4 Integration of the HMI with Safety Systems
8.7 Risk Analysis and Mitigation Strategies
8.8 Handover: Principles and Design for a Seamless Transition
8.8 Reliability: Designing Robust and Reliable HMI Systems
8.3 Explanation: Techniques for Improving System Explainability
8.4 Assisted Driving: HMI Integration for a Safe Experience
8.5 User-Centered Design: Principles and Methodologies
8.6 HMI Testing and Validation: Quality Assurance and Performance
8.7 Contextual Handover: Adaptation to Different Scenarios
8.8 Human Reliability: Factors Affecting User Confidence
8.8 Visual Explanation: Designing Intuitive Interfaces
8.80 Assisted Driving: Challenges and Solutions in the User Interface
9.9 Handover: Concepts and Protocols in Human-Machine Interaction
9.9 Interface Design to Facilitate Control Transfer
9.3 Levels of Trust in Autonomous Driving Systems
9.4 Factors Influencing User Trust
9.5 Strategies for Improving Trust in the HMI
9.6 Explainability: Communication of System Decisions
9.7 Supervised Driving: The Driver’s Role in the Process
9.8 Evaluation and Validation of HMI Systems
9.9 Practical Examples and Case Studies
9.90 The Future of HMI: Trends and Challenges
1.1 Introduction to Handover in Autonomous Driving Systems (L2–L4)
1.2 User Interface (UI) Design for Handover: Best Practices
1.3 Handover: Strategies for Seamless Transitions Between the System and the Driver
1.4 Handover: Testing and Validation of Transfer Functionality
1.5 Trust in Autonomous Driving: Key Factors
1.6 User Interface (UI) Design for Trust
1.7 Measurement and Evaluation of User Trust
1.8 Handover and Trust: Case Studies and Applications
1.9 Handover and Trust: Challenges and the Future of the Industry
1.10 Final Project: Development and Evaluation of an HMI System for Handover and Trust
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, DO-178C planning toolchains.
- SEIUM Laboratories: scale rotor test bench, vibration/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees and scholarships
**HMI Engineer for Autonomous Vehicles** (handover, trust, explainability, supervised driving
- Profile: Degree in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and information retrieval systems is a plus.
- Documents: Updated resume, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: application → technical evaluation of profile and experience → technical interview → review of case studies → final decision → enrollment.
- Fees:
- Lump-sum payment: 10% discount.
- Payment in 3 installments: no fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
- Monthly payment: available with a 7% fee on the total; annual review.
- Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications”, “Scholarships & Financial Aid”, and “Tuition & Financing” in the SEIUMa) mega-menu
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F. A. Q
Frequently Asked Questions
Yes, we have international certification.
Yes: experimental models, real-world data, applied simulations, professional environments, real-world case studies.
It is not required. We offer leveling tracks and mentoring
Absolutely. It covers electric propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortia.
Yes. Online/hybrid format with scheduled labs and visa support (see “Visa & Residence”).