Postgraduate Diploma in Domain Randomisation and Stochastic Coverage
Online 800-hour Postgraduate Diploma in Domain Randomisation and Stochastic Coverage. 8 modules with ISO 21448:2022, ISO/PAS 8800:2024 and CARLA. Assigned tutor and open enrolment.
Postgraduate Diploma in Domain Randomisation and Stochastic Coverage — studying from New Zealand
Autonomy and advanced driver assistance work in New Zealand is mostly applied rather than manufacturing-led, with significant interest in autonomous systems for agriculture, forestry, mining and port logistics. Research groups at the University of Auckland and the University of Canterbury contribute to robotics, perception and control, and firms in these regions develop software for driverless and remote-operated machinery. Engineers in this area often collaborate with government transport agencies on the regulatory frameworks that will govern automated vehicles on public roads.
Fees for New Zealand: NZ$3,550 (New Zealand dollar), with instalments and employer sponsorship available.
Relevant bodies in New Zealand
Typical roles
Questions from New Zealand
Which New Zealand bodies oversee autonomous and ADAS vehicle work, and how is engineering registration handled?
Engineering registration in New Zealand is handled by Engineering New Zealand, which offers Chartered Professional Engineer status. Land transport rules, including those affecting vehicle technology, are administered by NZ Transport Agency Waka Kotahi. SEIUM is a private institute and its qualifications are not state degrees or accredited by a national body, so recognition is ultimately a matter for employers or the relevant body.
Who employs autonomous vehicle and ADAS engineers in New Zealand?
Demand comes mainly from vehicle importers and fleet operators exploring driver assistance, software suppliers, research organisations and firms developing mobility or automation systems. Employers typically look for software, sensing and systems skills backed by project evidence. A private online qualification may help show your learning, but it does not guarantee a role or any particular salary.
Is this programme workable online from New Zealand with a full-time job?
Yes. The programmes are 100% online and taught in English, so you can study from New Zealand without needing a student visa. You will have an assigned technical tutor and complete a final project. Specialist courses run 300 hours, diplomas 800 hours and master's programmes 1,900 hours. Fees can be paid in instalments, and your employer may be willing to sponsor you.
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
Model scenario variability using probability distributions.
Apply domain randomisation to improve generalisation.
Use efficient sampling techniques for rare events.
Search for critical scenarios using optimisation and falsification.
Measure the stochastic coverage of a campaign.
Evaluate the robustness of neural networks against perturbations.
Integrate stochastic coverage into the safety case.
Who it's for
- Virtual validation engineer wishing to specialise in Domain Randomisation and Stochastic Coverage.
- Stochastic simulation specialist wishing to specialise in Domain Randomisation and Stochastic Coverage.
- Perception engineer wishing to specialise in Domain Randomisation and Stochastic Coverage.
- Safety data scientist wishing to specialise in Domain Randomisation and Stochastic Coverage.
- SOTIF engineer wishing to specialise in Domain Randomisation and Stochastic Coverage.
- AI robustness researcher wishing to specialise in Domain Randomisation and Stochastic Coverage.
- Graduates in engineering or science seeking to join car manufacturers.
What you'll work with
- ISO 21448:2022
- ISO/PAS 8800:2024
- ISO 34502:2022
- ISO/IEC TR 24029-1:2021
- ASAM OpenSCENARIO
- UL 4600
- ISO 26262:2018
8 modules · 800 h
1.1Environment, traffic and sensor variability1.2Aleatory and epistemic uncertainty1.3Parameter space of a logical scenario1.4Probability distributions of parameters1.5Dependencies and correlations between parameters1.6The rare-event problem1.7Relationship with SOTIF insufficiencies1.8Role of stochastic simulation in the safety case1.9Common sampling errors1.10Case study: parameter space of an overtaking manoeuvre
2.1Origins of the technique in robotics2.2Randomisation of textures, lighting and camera2.3Structured versus uniform randomisation2.4Randomisation of geometry and distractors2.5Effect on sim-to-real generalisation2.6Adaptive and automatic randomisation2.7Combination with domain adaptation2.8Measuring the resulting robustness2.9Risks of excessive randomisation2.10Lab: detector trained on random textures
3.1Simple and stratified Monte Carlo3.2Latin hypercube and quasi-random sequences3.3Importance sampling and cross-entropy3.4Subset methods for small probabilities3.5Markov chains and MCMC sampling3.6Stochastic models of traffic behaviour3.7Generation with learned generative models3.8Failure probability estimation and intervals3.9Computational cost by method3.10Practice: failure estimation with importance sampling
4.1Criticality and fitness functions4.2Bayesian optimisation over scenarios4.3Genetic and evolutionary algorithms4.4Adversarial reinforcement learning4.5Search for failure boundaries4.6Diversity versus criticality4.7Falsification with signal temporal logic4.8Validation of critical scenarios found4.9Integration into the simulation chain4.10Workshop: falsification of an ACC with Bayesian optimisation
5.1Coverage of parameters and combinations5.2Coverage of situations and the ODD5.3Neuron-criteria-based coverage5.4Entropy and diversity of the test set5.5Coverage of decision boundaries5.6Stopping criteria based on convergence5.7Coverage of unknown scenarios5.8Coverage visualisation5.9Communicating coverage in the safety case5.10Practice: coverage map of a Monte Carlo sweep
6.1Natural versus adversarial perturbations6.2Robustness assessment per ISO/IEC TR 24029-16.3ImageNet-C-type corrupted sets applied to driving6.4Formal verification of networks with bounds6.5Data augmentation for robustness6.6Uncertainty calibration6.7Out-of-distribution input detection6.8Temporal robustness in video sequences6.9Robustness requirements in ISO/PAS 88006.10Case study: robustness assessment of a segmenter under rain
7.1Simulation budget allocation7.2Automated randomisation pipelines7.3Traceability of seeds and configurations7.4Credibility of stochastic models7.5Combination with track testing7.6Statistical arguments for the authority7.7Repetition after software changes7.8Indicators for technical management7.9Risks and limitations of the approach7.10Workshop: randomisation pipeline with automated reports
8.1Selection of function and scenarios8.2Definition of parameter distributions8.3Design of domain randomisation8.4Stochastic sampling campaign8.5Search for critical scenarios8.6Coverage analysis8.7Failure probability estimation8.8Argument for the safety case8.9Report 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. 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 CARLA, Python with NumPy and SciPy, Optuna, Foretellix Foretify, PyTorch, in free, academic or evaluation versions. The tests are studied through real procedures for Monte Carlo simulation campaigns, automatic search for critical scenarios, and robustness assessment of perception networks. 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.
Model scenario variability using probability distributions.
Apply domain randomisation to improve generalisation.
Use efficient sampling techniques for rare events.
Search for critical scenarios using optimisation and falsification.
Measure the stochastic coverage of a campaign.
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.
- Sufficient English to follow the reading of technical standards.
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 is 800 hours in total, spread over 6 to 8 months. That is on average between 10 and 15 hours per week, compatible with a full-time job.
You will work with CARLA, Python with NumPy and SciPy, Optuna, Foretellix Foretify, PyTorch. Free, academic or evaluation versions are used, and the tutor indicates the open alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrating 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 credited towards the advanced programme. Check the credit recognition policy when you enrol.
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