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Course in Trust and Explainability Regulations in ADAS

Online 300 h course in trust and explainability regulations in ADAS. 6 modules covering Regulation (EU) 2019/2144 (general vehicle safety), UN Regulation No. 171 (DCAS) and Python (SHAP). Assigned tutor and open enrolment.

Duration300 h
ModeOnline
Start datesSet by the faculty
AwardSEIUM Institute certificate
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Learning outcomes

What you will be able to do

01

Explain the factors that determine driver trust in ADAS.

02

Apply explainability techniques to perception and decision functions.

03

Interpret the regulations applicable to ADAS and AI systems.

04

Measure trust and misuse through user studies.

05

Design strategies for communicating the system status.

06

Assess risks of overtrust or undertrust.

07

Develop a concept for an explainable ADAS.

Profile

Who it's for

  • ADAS systems engineer who wants to specialise in Trust and Explainability Regulations in ADAS.
  • Human factors specialist who wants to specialise in Trust and Explainability Regulations in ADAS.
  • Explainable AI researcher who wants to specialise in Trust and Explainability Regulations in ADAS.
  • User experience engineer who wants to specialise in Trust and Explainability Regulations in ADAS.
  • ADAS regulatory analyst who wants to specialise in Trust and Explainability Regulations in ADAS.
  • Validation engineer who wants to specialise in Trust and Explainability Regulations in ADAS.
  • Engineering or science graduates looking to enter the automotive industry.
Standards & tools

What you'll work with

  • Regulation (EU) 2019/2144 (general vehicle safety)
  • UN Regulation No. 171 (DCAS)
  • UN Regulation No. 157 (ALKS)
  • SAE J3016 (levels of automation)
  • Regulation (EU) 2024/1689 (AI Act)
  • ISO 21448 (SOTIF)
Syllabus

6 modules · 300 h

  1. 1.1Calibrated trust, overtrust and undertrust
  2. 1.2Trust models in automation
  3. 1.3Overconfidence and misuse of ADAS
  4. 1.4Commercial names and expectations
  5. 1.5NHTSA and NTSB investigations
  6. 1.6Driver situational awareness
  7. 1.7Human factors in Level 2 and 3 systems
  8. 1.8Measuring user trust
  9. 1.9Manufacturer communication
  10. 1.10Case study: analysis of a Level 2 accident
  1. 2.1What explaining means in an ADAS
  2. 2.2Explainability of perception networks
  3. 2.3Interpretability of decision modules
  4. 2.4Post hoc methods and their limits
  5. 2.5Uncertainty and model confidence
  6. 2.6Explanations in the driver HMI
  7. 2.7DSSAD records and reconstruction
  8. 2.8ISO/PAS 8800 and AI in automotive
  9. 2.9Technical documentation for type approval
  10. 2.10Laboratory: visualising uncertainty in perception
  1. 3.1UNECE R79 and steering functions
  2. 3.2UNECE R171 DCAS Regulation
  3. 3.3UNECE R157 ALKS Regulation
  4. 3.4Driver monitoring requirements
  5. 3.5Regulation (EU) 2024/1689 on AI
  6. 3.6Product liability for defective products Directive
  7. 3.7Advertising and communication requirements
  8. 3.8Euro NCAP Assisted Driving
  9. 3.9ISO 21448 and foreseeable misuse
  10. 3.10Practice: regulatory matrix for a DCAS system
  1. 4.1Driving simulator studies
  2. 4.2On-road trials with users
  3. 4.3Trust and acceptance scales
  4. 4.4Takeover time metrics
  5. 4.5Gaze and distraction analysis
  6. 4.6Assessment of reported failure modes
  7. 4.7Designing tests with novice users
  8. 4.8Ethics of human trials
  9. 4.9Statistical analysis of results
  10. 4.10Workshop: designing a simulator study
  1. 5.1System status indicators
  2. 5.2Graduated warnings and escalation
  3. 5.3User training and manuals
  4. 5.4In-vehicle onboarding
  5. 5.5Transparency of ODD limitations
  6. 5.6Communicating updates
  7. 5.7Managing expectations in marketing
  8. 5.8Fleet feedback and continuous improvement
  9. 5.9Shared driver-system responsibility
  10. 5.10Case study: redesigning Level 2 warnings
  1. 6.1Selecting the ADAS system
  2. 6.2Regulatory requirements analysis
  3. 6.3Identifying misuse risks
  4. 6.4Proposing explanations
  5. 6.5HMI design
  6. 6.6User evaluation plan
  7. 6.7Documentation plan
  8. 6.8Final recommendations
  9. 6.9Report and presentation
  10. 6.10Final review with the tutor
Methodology, software & labs

How you'll learn

100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, which makes it possible to combine with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (SHAP), CARLA, MATLAB, Tobii Pro Lab, in free, academic or evaluation versions. Assignments are based on real procedures for driving simulator studies, user trust surveys and assessment of explanations in the interface. Assessment is continuous by module, plus the final integrative project.

Accredited laboratories(EN ISO/IEC 17025) with a rotor test bench, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented final project:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, OT cyber, DIH and compliance as pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Selecting the ADAS system

Explain the factors that determine driver trust in ADAS.

Regulatory requirements analysis

Apply explainability techniques to perception and decision functions.

Identifying misuse risks

Interpret the regulations applicable to ADAS and AI systems.

Proposing explanations

Measure trust and misuse through user studies.

HMI design

Design strategies for communicating the system status.

Career opportunities

Where this takes you

ADAS systems engineerHuman factors specialistExplainable AI researcherUser experience engineerADAS regulatory analystValidation engineer
Admissions & scholarships

Requirements

  • No prior degree is required.
  • Internet connection and a computer capable of running the programme's software.
  • Sufficient Spanish to follow technical regulatory texts.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 300 hours spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.

The work uses Python (SHAP), CARLA, MATLAB and Tobii Pro Lab. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

A SEIUM own diploma upon passing the continuous assessment and the integrative project. It is in-house training, not an official Ministry qualification.

Yes. Modules passed in a Course or Postgraduate Diploma in the same area are recognised towards the higher programme. Check the recognition policy when you enrol.

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