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Postgraduate DiplomaData, AI, Simulation & Analytics Applications open

Diploma in Ray-Tracing, Domain Randomization and Coverage

Online postgraduate diploma of 800 hours in ray-tracing, domain randomisation and coverage. 8 modules covering synthetic data for perception, ray-tracing and physically based rendering, 3D scenes and assets, domain randomisation, scenario coverage, sensor simulation and safety evaluation, with an assigned technical tutor.

Duration800 hours · 6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Justify the use of synthetic data in perception systems.

02

Apply ray-tracing and physically based rendering techniques.

03

Build 3D scenes with standard assets and formats.

04

Design effective domain randomisation strategies.

05

Measure scenario coverage against the operational design domain.

06

Simulate camera, LiDAR and radar sensors with controlled fidelity.

07

Evaluate the contribution of synthetic data to system safety.

Profile

Who it's for

  • Synthetic data engineers building datasets for perception models.
  • Computer vision engineers who need to close the domain gap.
  • Sensor simulation engineers working with camera, LiDAR and radar.
  • ADAS validation engineers responsible for scenario coverage.
  • Technical 3D artists moving into simulation for AI.
  • Perception engineers in robotics and autonomous systems.
  • Engineering or science graduates seeking to enter the automotive sector.
Standards & tools

What you'll work with

  • ISO 21448:2022 (SOTIF) (safety of the intended functionality of road vehicles)
  • ISO 34502:2022 (scenario-based safety evaluation framework for automated driving)
  • ISO 34503:2023 (taxonomy for the operational design domain)
  • ASAM OpenSCENARIO (description format for dynamic driving scenarios)
  • ASAM OpenDRIVE (road network description format)
  • ASAM OSI (open simulation interface for sensor models)
  • ISO/IEC 12113:2022 (glTF 2.0) (3D asset transmission format)
  • UL 4600 (safety standard for autonomous products)
  • NVIDIA Omniverse Replicator
  • CARLA
  • Blender
  • NVIDIA Isaac Sim
  • Unreal Engine
Syllabus

8 modules

  1. 1.1The need for synthetic data
  2. 1.2Camera, LiDAR and radar sensors
  3. 1.3Automatic annotation in simulation
  4. 1.4The visual domain gap
  5. 1.5Types of scenes and environments
  6. 1.6Game engines versus dedicated simulators
  7. 1.7ASAM scenario standards
  8. 1.8Relationship with the operational design domain
  9. 1.9Value indicators for synthetic data
  10. 1.10Workshop: specifying a synthetic data generator
  1. 2.1The rendering equation
  2. 2.2Path tracing and Monte Carlo sampling
  3. 2.3BRDFs and physical materials
  4. 2.4Global illumination and HDR
  5. 2.5Acceleration with BVH and RT Cores
  6. 2.6Denoising of rendered images
  7. 2.7Simulating camera effects
  8. 2.8Ray-tracing for LiDAR and radar
  9. 2.9Performance versus fidelity
  10. 2.10Lab: physically based render of a scene in Blender Cycles
  1. 3.1OpenUSD and glTF formats
  2. 3.2Asset libraries and licences
  3. 3.3Procedural generation of environments
  4. 3.4PBR materials and textures
  5. 3.5Animated pedestrians and vehicles
  6. 3.6Weather and lighting conditions
  7. 3.7Managing large-scale scenes
  8. 3.8Geometric validation of assets
  9. 3.9Scenarios from real-world data
  10. 3.10Practical exercise: procedural scene with NVIDIA Omniverse Replicator
  1. 4.1Randomising textures and lighting
  2. 4.2Randomising positions and occlusions
  3. 4.3Randomising sensor parameters
  4. 4.4Context-structured randomisation
  5. 4.5Performance-guided randomisation
  6. 4.6Parameter ranges and distributions
  7. 4.7Balancing realism and variety
  8. 4.8Transfer to real data
  9. 4.9Ablations of randomised factors
  10. 4.10Lab: detector trained with domain randomisation
  1. 5.1Scenario parameter space
  2. 5.2ODD taxonomy according to ISO 34503
  3. 5.3Scenario-based testing according to ISO 34502
  4. 5.4Situation coverage metrics
  5. 5.5Edge cases and rare events
  6. 5.6Adaptive sampling of critical scenarios
  7. 5.7Combinatorics and pairwise testing
  8. 5.8Data distribution coverage
  9. 5.9Traceability of scenarios to requirements
  10. 5.10Practical exercise: coverage matrix for an urban ODD
  1. 6.1Camera noise models
  2. 6.2Distortion and motion blur
  3. 6.3LiDAR simulation with reflectivity
  4. 6.4Radar and radar cross-section models
  5. 6.5ASAM OSI interface
  6. 6.6CARLA and Isaac Sim simulators
  7. 6.7Validating simulated sensor models
  8. 6.8Multi-sensor synchronisation in simulation
  9. 6.9Real-time performance
  10. 6.10Case study: simulating a LiDAR in CARLA
  1. 7.1Detection and segmentation metrics
  2. 7.2Evaluation by ODD subsets
  3. 7.3Robustness testing under adverse conditions
  4. 7.4SOTIF and triggering conditions
  5. 7.5Data sufficiency arguments
  6. 7.6Governance of the synthetic dataset
  7. 7.7Documenting the generator
  8. 7.8Integration into validation pipelines
  9. 7.9Limitations of synthetic data
  10. 7.10Case study: coverage evidence for an ADAS system
  1. 8.1Defining the ODD and requirements
  2. 8.2Building scenes and assets
  3. 8.3Rendering configuration
  4. 8.4Domain randomisation strategy
  5. 8.5Dataset generation and annotation
  6. 8.6Model training and evaluation
  7. 8.7Scenario coverage analysis
  8. 8.8Conclusions on the real-world gap
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

Training is 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 the programme fits alongside your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is NVIDIA Omniverse Replicator, CARLA, Blender, NVIDIA Isaac Sim and Unreal Engine, in free, academic or evaluation versions. Case studies are based on real procedures: generating synthetic data for object detection, training with domain randomisation and coverage analysis of driving scenarios. Assessment is continuous by module, plus the final integrating project.

Standards covered: ISO 21448:2022 (SOTIF), ISO 34502:2022, ISO 34503:2023, ASAM OpenSCENARIO, ASAM OpenDRIVE, ASAM OSI, ISO/IEC 12113:2022 (glTF 2.0), UL 4600

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 21448 (SOTIF), ISO 34502 and ISO 34503 applied to real cases, not as a theoretical annex.
Industry software:NVIDIA Omniverse Replicator, CARLA, Blender, NVIDIA Isaac Sim and Unreal Engine, in free, academic or evaluation versions.
Final project:a synthetic dataset for a defined ODD, from scene building and domain randomisation to model training and scenario coverage analysis, 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.

ODD and requirements
Define the operational design domain and the perception requirements.
Scenes and rendering
Build the scenes and assets and configure physically based rendering.
Randomisation and dataset
Apply a domain randomisation strategy and generate an annotated dataset.
Training and coverage
Train and evaluate the model and analyse scenario coverage.
Final project
Conclusions on the real-world gap, written up in a report and presentation reviewed by your tutor.
Career opportunities

Where this takes you

Synthetic data engineerComputer vision engineerSensor simulation engineerADAS validation engineerTechnical 3D artistPerception 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 NVIDIA Omniverse Replicator, CARLA, Blender, NVIDIA Isaac Sim and Unreal Engine, 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 programme 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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