Postgraduate Diploma in Risk-Based Scenario Catalogues and ODD KPIs
Online 800-hour Postgraduate Diploma in Risk-Based Scenario Catalogues and ODD KPIs. 8 modules covering ISO 34502:2022, ISO 34503:2023 and esmini. Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Apply the scenario-based approach to ADS validation.
Extract scenarios from accident databases and naturalistic driving data.
Prioritise scenarios according to exposure and severity.
Model interoperable catalogues using ASAM formats.
Define ODD coverage and exposure KPIs.
Argue the sufficiency of testing.
Maintain the catalogue with field data.
Who it's for
- Scenario engineer looking to specialise in Risk-Based Scenario Catalogues and ODD KPIs.
- ADS safety analyst looking to specialise in Risk-Based Scenario Catalogues and ODD KPIs.
- Validation engineer looking to specialise in Risk-Based Scenario Catalogues and ODD KPIs.
- Driving data scientist looking to specialise in Risk-Based Scenario Catalogues and ODD KPIs.
- Test catalogue manager looking to specialise in Risk-Based Scenario Catalogues and ODD KPIs.
- Homologation consultant looking to specialise in Risk-Based Scenario Catalogues and ODD KPIs.
- Engineering or science graduates seeking to join car manufacturers.
What you'll work with
- ISO 34502:2022
- ISO 34503:2023
- ISO 34501:2022
- ASAM OpenSCENARIO
- ASAM OpenDRIVE
- ISO 21448:2022
- UL 4600
- Reglamento (UE) 2022/1426
8 modules · 800 h
1.1Limitations of mileage-based validation1.2Functional, logical and concrete scenarios1.3Vocabulary of ISO 345011.4Layers of the PEGASUS scenario model1.5Nominal, critical and failure scenarios1.6Relationship between scenarios and ODD1.7UNECE's NATM multi-pillar method1.8Uses of the catalogue in development and homologation1.9European initiatives and public databases1.10Case study: decomposition of an urban crossing scenario
2.1Accident databases: GIDAS, CIDAS and NHTSA CRSS2.2Naturalistic driving data2.3Public datasets: highD, inD and rounD2.4Automatic extraction from fleet data2.5Expert knowledge and hazard analysis2.6Scenarios derived from regulation and Euro NCAP2.7Adversarial scenarios generated by optimisation2.8Merging and de-duplication of sources2.9Biases and gaps in each source2.10Lab: extracting cut-ins from highD
3.1ISO 34502 methodology3.2Disturbance factors for perception, traffic and vehicle3.3Exposure estimation from data3.4Severity using injury models3.5Risk matrices per scenario3.6Allocation of test effort by risk3.7Scenarios involving vulnerable road users3.8Periodic review of prioritisation3.9Documentation of criteria3.10Workshop: risk ranking of forty logical scenarios
4.1ASAM OpenSCENARIO 1.x and 2.04.2Road network with ASAM OpenDRIVE4.3Parameter distributions and correlations4.4Scenario metadata and tagging4.5Tag taxonomy and search4.6Scenario databases and API4.7Versioning and catalogue governance4.8Interoperability between simulators4.9Scenario quality and validity4.10Practice: tagged catalogue in OpenSCENARIO 2.0
5.1ODD specification with ISO 345035.2ODD coverage indicators5.3Fleet exposure by ODD attribute5.4ODD exit rate and transitions5.5Criticality metrics: TTC, THW, PET and DRAC5.6Leading and lagging safety indicators5.7UL 4600 safety performance indicators5.8Thresholds and targets per indicator5.9Dashboards for management and authorities5.10Workshop: ODD KPI dashboard for an urban shuttle
6.1Coverage of the parameter space6.2Combinatorics and design of experiments6.3Adaptive sampling and boundary search6.4Campaign stopping criteria6.5Allocating scenarios to simulation, track and road6.6Sufficiency arguments for the safety case6.7Unknown scenarios and continuous learning6.8Novelty metrics in field data6.9Communicating coverage gaps6.10Case study: coverage analysis of an ALKS
7.1Feedback from field data7.2Incorporating incidents and near misses7.3Updates due to ODD or software changes7.4Scenario regression management7.5Sharing scenarios between organisations7.6Requirements of Regulation (EU) 2022/14267.7Third-party catalogue audit7.8Intellectual property and data licensing7.9Roles and catalogue governance7.10Practice: procedure for adding a field scenario
8.1Definition of the system and its ODD8.2Selection of scenario sources8.3Risk-based prioritisation8.4Modelling in ASAM formats8.5Definition of ODD KPIs8.6Coverage analysis8.7Catalogue maintenance plan8.8Report for the safety case8.9Thesis and presentation8.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is esmini, Foretellix Foretify, IPG CarMaker, Python with pandas, dSPACE ModelDesk, in free, academic or evaluation versions. Practicals cover real procedures for extracting scenarios from naturalistic data, prioritising scenarios by risk, and analysing coverage of the operational domain. Assessment is continuous per module plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Apply the scenario-based approach to ADS validation.
Extract scenarios from accident databases and naturalistic driving data.
Prioritise scenarios according to exposure and severity.
Model interoperable catalogues using ASAM formats.
Define ODD coverage and exposure KPIs.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Spanish language level sufficient to read technical regulatory texts.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 800 hours spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
The programme uses esmini, Foretellix Foretify, IPG CarMaker, Python with pandas, dSPACE ModelDesk. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
An own diploma from SEIUM upon passing the continuous assessment and the integrating project. It is in-house training, not an official Ministry title.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the higher programme. Check the credit transfer policy when you enrol.
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