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Postgraduate DiplomaAutonomous, ADAS & Software-Defined Vehicle Applications open

Postgraduate Diploma in Sim↔Real-World Correlation Metrics

Online postgraduate diploma of 800 hours in sim-to-real correlation metrics. 8 modules covering model verification and validation, signal and trajectory metrics, vehicle dynamics, sensor and scenario-level correlation, uncertainty quantification and credibility management, with Regulation (EU) 2022/1426, ISO 18571:2014 and ISO 26262-8:2018, with an assigned technical tutor.

Duration800 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Plan the validation of simulation models.

02

Apply objective metrics to compare signals and trajectories.

03

Correlate vehicle dynamics models with physical tests.

04

Assess the correlation of sensor and perception models.

05

Compare system behaviour at scenario level.

06

Quantify the uncertainty of models and measurements.

07

Document simulation credibility for third parties.

Profile

Who it's for

  • Simulation correlation engineers who want a rigorous, standards-based method.
  • Vehicle dynamics engineers responsible for calibrating and validating models.
  • Virtual validation engineers working on ADAS and automated driving.
  • Sensor modelling specialists who need to measure the reality gap.
  • Credibility analysts preparing evidence for safety cases.
  • Virtual type-approval engineers dealing with technical services.
  • Engineering or science graduates looking to enter vehicle manufacturing.
Standards & tools

What you'll work with

  • Regulation (EU) 2022/1426 (type approval of automated driving systems)
  • ISO 18571:2014 (objective rating metric for non-ambiguous signals)
  • ISO 26262-8:2018 (functional safety supporting processes, tool qualification)
  • NASA-STD-7009 (standard for models and simulations)
  • ASME V&V 20 (verification and validation in computational modelling)
  • ISO 3888-1 (double lane-change test track)
  • ASAM OSI (Open Simulation Interface)
  • ISO 21448:2022 (safety of the intended functionality, SOTIF)
  • MATLAB/Simulink
  • IPG CarMaker
  • Python with SciPy
  • VI-grade VI-CarRealTime
  • esmini
Syllabus

8 modules · 800 h

  1. 1.1The difference between verification and validation
  2. 1.2Validation hierarchy from component to system
  3. 1.3Domain of validity and domain of use
  4. 1.4Sources of error: numerical, model and input
  5. 1.5NASA-STD-7009 standard for models and simulations
  6. 1.6The ASME V&V 20 approach
  7. 1.7Credibility of the virtual toolchain
  8. 1.8UNECE credibility requirements for ADS
  9. 1.9Planning validation experiments
  10. 1.10Case study: validation plan for a vehicle model
  1. 2.1Root mean square error and maximum error
  2. 2.2Cross-correlation and time lag
  3. 2.3Dynamic time warping (DTW)
  4. 2.4ISO 18571 metrics for curves
  5. 2.5Fréchet distance between trajectories
  6. 2.6Frequency-domain comparison
  7. 2.7Metrics for bus signals
  8. 2.8Noise handling and filtering
  9. 2.9Selecting metrics per variable
  10. 2.10Laboratory: comparing manoeuvres with ISO 18571
  1. 3.1Reference tests: ISO 3888 and steady state
  2. 3.2Tyre parameter identification
  3. 3.3Steering and suspension models
  4. 3.4Braking and acceleration response
  5. 3.5Comparison of lateral accelerations and yaw
  6. 3.6Sensitivity to uncertain parameters
  7. 3.7Calibration through optimisation
  8. 3.8Cross-validation with other manoeuvres
  9. 3.9Documenting the range of validity
  10. 3.10Practical exercise: correlating a double lane change
  1. 4.1Raw-data-level metrics
  2. 4.2Comparison of lidar point clouds
  3. 4.3Radar signatures and radar cross-section
  4. 4.4Image metrics: PSNR, SSIM and FID
  5. 4.5Object-list-level correlation
  6. 4.6Comparison of detections and false positives
  7. 4.7The perception gap between real and synthetic data
  8. 4.8Reference tests with standardised targets
  9. 4.9Influence of weather on correlation
  10. 4.10Workshop: correlating real and virtual camera detections
  1. 5.1Reconstructing real scenarios in simulation
  2. 5.2System behaviour metrics
  3. 5.3Comparison of TTC and minimum distances
  4. 5.4Consistency of decisions and activations
  5. 5.5Statistical correlation over many scenarios
  6. 5.6Hypothesis and equivalence testing
  7. 5.7Acceptance tolerances
  8. 5.8Non-correlation cases and their analysis
  9. 5.9Feedback to the model
  10. 5.10Case study: track-to-simulation correlation of an AEB campaign
  1. 6.1Propagation of input uncertainty
  2. 6.2Monte Carlo methods and polynomial chaos
  3. 6.3Measurement uncertainty on the test track
  4. 6.4Confidence bands in comparisons
  5. 6.5Global sensitivity analysis and Sobol indices
  6. 6.6Validation area metrics
  7. 6.7Model uncertainty and extrapolation
  8. 6.8Presenting results with uncertainty
  9. 6.9Decision-making under uncertainty
  10. 6.10Practical exercise: sensitivity analysis of a braking model
  1. 7.1Model credibility report
  2. 7.2Independent assessment of the simulation
  3. 7.3Version management of validated models
  4. 7.4Revalidation after changes
  5. 7.5Integration into the safety case
  6. 7.6Communication with technical services
  7. 7.7Qualification according to ISO 26262-8
  8. 7.8Correlation database
  9. 7.9Credibility indicators
  10. 7.10Workshop: credibility dossier for type approval
  1. 8.1Selecting the model and its intended use
  2. 8.2Plan of reference experiments
  3. 8.3Acquisition and preparation of real data
  4. 8.4Running equivalent simulations
  5. 8.5Calculating correlation metrics
  6. 8.6Uncertainty quantification
  7. 8.7Credibility report
  8. 8.8Recommendations for model improvement
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

The programme is delivered 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so it fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. You work with MATLAB/Simulink, IPG CarMaker, Python with SciPy, VI-grade VI-CarRealTime and esmini, in free, academic or evaluation versions. Testing is studied through real procedures: correlating vehicle dynamics manoeuvres, comparing detections from real and simulated sensors, and reconstructing test-track scenarios in simulation. Assessment is continuous by module, plus the final project.

Standards covered: Regulation (EU) 2022/1426, ISO 18571:2014, ISO 26262-8:2018, NASA-STD-7009, ASME V&V 20, ISO 3888-1, ASAM OSI, ISO 21448:2022.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2022/1426, ISO 18571:2014 and ISO 26262-8:2018 applied to real cases, not as a theoretical annex.
Industry software:MATLAB/Simulink, IPG CarMaker, Python with SciPy, VI-grade VI-CarRealTime and esmini, in free, academic or evaluation versions.
Final project:A complete sim-to-real correlation study of a model, with metrics, uncertainty quantification and a credibility report, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

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

Model and intended use
Select the model, define its intended use and plan the reference experiments.
Real data preparation
Acquire and prepare real test data for comparison with simulation.
Correlation metrics
Run equivalent simulations and calculate objective correlation metrics such as ISO 18571.
Uncertainty quantification
Quantify model and measurement uncertainty and assess its effect on the comparison.
Final project
Present the credibility report with recommendations for improving the model.
Career opportunities

Where this takes you

Simulation correlation engineerVehicle dynamics engineerVirtual validation engineerSensor modelling specialistCredibility analystVirtual type-approval engineer
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. A university degree is not required.
  • An internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

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 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with MATLAB/Simulink, IPG CarMaker, Python with SciPy, VI-grade VI-CarRealTime and esmini, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector (no university degree required), a computer able to run the software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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