Master'sRegulation, Safety, RAMS & Homologation Applications open

Master’s in ADAS and Autonomous Driving Engineering

Online master's programme of 1,900 hours in ADAS and autonomous driving engineering, structured in 12 modules covering vehicle sensors, perception, localisation and maps, prediction and planning, vehicle control, ADAS functions, system architectures, safety and validation, human factors and regulation, with an assigned technical tutor.

Duration1900 h

ModeOnline

LanguageEnglish

Start datesSet by the faculty

AwardSEIUM Institute diploma

Enrol now View syllabus Ask an advisor

Master’s in ADAS and Autonomous Driving Engineering — studying from New Zealand

Regulation, safety and reliability engineering is well established in New Zealand, underpinned by a strong regulatory framework for health and safety, transport, building and electrical equipment. WorkSafe New Zealand administers workplace health and safety law, while Standards New Zealand develops and publishes national standards. Professionals in this area support compliance, certification and risk assessment for manufacturers, infrastructure operators and public agencies.

Fees for New Zealand: NZ$17,700 (New Zealand dollar), with instalments and employer sponsorship available.

Relevant bodies in New Zealand

WorkSafe New Zealand
Standards New Zealand
Ministry of Business, Innovation and Employment (MBIE)

Typical roles

Safety Engineer
Compliance Engineer
Reliability Engineer
Homologation Specialist

Questions from New Zealand

Which New Zealand bodies set the rules for safety and compliance engineering?

WorkSafe New Zealand regulates workplace health and safety, and the NZ Transport Agency Waka Kotahi handles vehicle and transport compliance. Engineering New Zealand is the professional body, including the Chartered Professional Engineer pathway. SEIUM's qualifications are private and not accredited by any national body, so their recognition is decided by employers or the relevant body.

Do New Zealand employers hire RAMS and safety engineers?

Yes. Reliability, availability, maintainability and safety roles exist across infrastructure, energy, transport, and manufacturing. Employers typically look for demonstrated competence against recognised standards and professional registration where relevant. A private qualification can support an application, but employers decide how much it counts.

Can I study this alongside work, and do I need a visa?

Yes to both parts. The programme is 100% online and taught in English, so no student visa is needed to study from New Zealand. You get an assigned technical tutor and complete a final project. Fees are payable in instalments, and employer sponsorship is possible. Programme lengths range from 300 to 1,900 hours.

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Learning outcomes

What you will be able to do

Understand the levels and architectures of automated driving.

Select and calibrate vehicle sensors.

Develop perception and localisation algorithms.

Implement motion planning and control.

Specify ADAS functions.

Validate systems through scenarios and simulation.

Analyse the regulatory framework for automated driving.

Profile

Who it's for

Autonomous driving engineers at manufacturers and technology suppliers.
Perception engineers working with cameras, radar and lidar.
Planning and control engineers.
ADAS validation engineers.
Automotive software architects.
Sensor fusion specialists.
Engineering or science graduates aiming to enter the automotive industry.

Standards & tools

What you'll work with

SAE J3016:2021 (taxonomy of driving automation)
ISO 26262:2018 (road vehicle functional safety)
ISO 21448:2022 (SOTIF, safety of the intended functionality)
UNECE Regulation No. 157 (Automated Lane Keeping Systems)
Implementing Regulation (EU) 2022/1426 (type approval of automated driving systems)
ISO 34502:2022 (scenario-based safety evaluation of automated driving systems)
UL 4600 (safety evaluation of autonomous products)
Regulation (EU) 2019/2144 (general vehicle safety)
CARLA
ROS 2
Autoware
MATLAB/Simulink (Automated Driving Toolbox)
Python (PyTorch)

Syllabus

12 modules · 1900 h

1.1SAE J3016 levels of automation
1.2Difference between ADAS and automated driving
1.3Use cases: motorway, city and logistics
1.4Industrial state of the art
1.5Robotaxis and commercial services
1.6Autonomous lorries on motorways
1.7Expected safety benefits
1.8Risks and limitations
1.9Technology roadmaps
1.10Case study: analysis of a commercial robotaxi deployment

2.1Cameras and their calibration
2.2Millimetre-wave radar
2.3Mechanical and solid-state lidar
2.4Ultrasonic sensors
2.5GNSS and inertial systems
2.6High-definition maps
2.7Sensor cleaning and degradation
2.8Adverse weather conditions
2.9Selecting the sensor suite
2.10Lab: camera–lidar calibration

3.1Object detection with deep learning
3.2Semantic image segmentation
3.3Point cloud perception
3.4Early and late sensor fusion
3.5Multi-object tracking
3.6Depth estimation
3.7Lane and sign detection
3.8Public datasets
3.9Perception metrics
3.10Practical exercise: object detection with a YOLO model

4.1GNSS-RTK localisation
4.2GNSS-IMU fusion with Kalman filters
4.3Visual and lidar SLAM
4.4Localisation in HD maps
4.5Map maintenance
4.6Map formats
4.7Required position accuracy
4.8Localisation integrity
4.9Failures and degradation
4.10Lab: localisation with an extended Kalman filter

5.1Agent trajectory prediction
5.2Route planning
5.3Behaviour planning
5.4Motion planning
5.5Trajectory optimisation
5.6Model predictive control
5.7Interaction with other road users
5.8End-to-end planning
5.9Comfort and safety constraints
5.10Practical exercise: lattice trajectory planner in Python

6.1Vehicle dynamic models
6.2Lateral vehicle control
6.3Longitudinal speed control
6.4Drive-by-wire
6.5Actuator redundancy
6.6Stability and grip limits
6.7Controller tuning
6.8In-vehicle validation
6.9Integration with ADAS
6.10Lab: lateral control with Stanley and Pure Pursuit

7.1Autonomous emergency braking
7.2Adaptive cruise control
7.3Lane keeping assist
7.4Lane change assist
7.5Traffic sign recognition
7.6Traffic jam assist
7.7Remote automated parking
7.8Driver monitoring
7.9Euro NCAP assessment
7.10Case study: functional specification of a motorway assistant

8.1Centralised and zonal architectures
8.2High-performance computers
8.3ROS 2 and AUTOSAR Adaptive middleware
8.4Redundancy and graceful degradation
8.5In-vehicle communications
8.6Software-defined vehicle
8.7Thermal management and power consumption
8.8Hardware and software integration
8.9Software updates
8.10Practical exercise: functional architecture of a Level 3 system

9.1ISO 26262 functional safety
9.2ISO 21448 SOTIF
9.3Scenario-based validation
9.4Large-scale simulation
9.5Track testing
9.6Public road testing
9.7Safety metrics
9.8System safety case
9.9UL 4600 standard
9.10Lab: scenario validation in CARLA

10.1Driver–system interaction
10.2Transition of control
10.3Driver situational awareness
10.4Overreliance
10.5Driver monitoring
10.6Communication with pedestrians
10.7External human–machine interfaces
10.8Acceptance and trust
10.9User training
10.10Workshop: designing a transition-of-control interface

11.1UNECE Regulation R157 ALKS
11.2Regulation (EU) 2022/1426 for automated driving systems
11.3Spanish testing regulations
11.4Regulation in Germany, the United Kingdom and the US
11.5Civil liability and insurance
11.6Data protection
11.7Ethics of automated driving
11.8Mandatory cybersecurity under R155
11.9Expected regulatory developments
11.10Case study: comparative analysis of regulatory frameworks

12.1Defining the function and ODD
12.2System architecture
12.3Perception and localisation
12.4Planning and control
12.5Safety analysis
12.6Validation in simulation
12.7Regulatory fit of the function
12.8Conclusions and future work
12.9Thesis report
12.10Master's Thesis: defence before a panel

Methodology, software & labs

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: you progress at your own pace within the intake, so the programme can be combined with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is CARLA, ROS 2, Autoware, MATLAB/Simulink (Automated Driving Toolbox) and Python (PyTorch), in free, academic or evaluation versions. Exercises are based on real procedures for developing perception and planning algorithms, scenario-based validation in simulation and sensor calibration. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: SAE J3016:2021 (taxonomy of driving automation), ISO 26262:2018, ISO 21448:2022 (SOTIF), UNECE Regulation No. 157 (ALKS), Implementing Regulation (EU) 2022/1426 (automated driving systems), ISO 34502:2022 (scenarios for automated driving systems), UL 4600 (safety evaluation of autonomous products), Regulation (EU) 2019/2144.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.

Standards-based learning:guided reading of SAE J3016, ISO 26262, ISO 21448, UNECE R157 and ISO 34502 applied to real cases, not as a theoretical annex.

Industry software:CARLA, ROS 2, Autoware, MATLAB/Simulink (Automated Driving Toolbox) and Python (PyTorch), in free, academic or evaluation versions.

Master's Thesis with defence:a complete automated driving function, from ODD and architecture to perception, planning, safety analysis and simulation validation, defended before a panel.

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.

Sensor calibration

Calibrate a camera–lidar pair and select the sensor suite for the target function.

Perception and localisation

Train a YOLO object detector and localise the vehicle with an extended Kalman filter.

Planning and control

Build a lattice trajectory planner and lateral control with Stanley and Pure Pursuit.

Scenario validation

Validate the function against safety scenarios in CARLA.

Master's Thesis

A complete automated driving function with safety analysis and regulatory fit, defended before a panel.

Career opportunities

Where this takes you

Autonomous driving engineerPerception engineerPlanning and control engineerADAS validation engineerAutomotive software architectSensor fusion specialist

Admissions & scholarships

Requirements

A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
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 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with CARLA, ROS 2, Autoware, MATLAB/Simulink (Automated Driving Toolbox) and Python (PyTorch), 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 Master's Thesis. 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 in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A degree in engineering, science or architecture, or equivalent professional experience, plus a suitable computer, an internet connection 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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