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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
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Learning outcomes

What you will be able to do

01

Understand the levels and architectures of automated driving.

02

Select and calibrate vehicle sensors.

03

Develop perception and localisation algorithms.

04

Implement motion planning and control.

05

Specify ADAS functions.

06

Validate systems through scenarios and simulation.

07

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. 1.1SAE J3016 levels of automation
  2. 1.2Difference between ADAS and automated driving
  3. 1.3Use cases: motorway, city and logistics
  4. 1.4Industrial state of the art
  5. 1.5Robotaxis and commercial services
  6. 1.6Autonomous lorries on motorways
  7. 1.7Expected safety benefits
  8. 1.8Risks and limitations
  9. 1.9Technology roadmaps
  10. 1.10Case study: analysis of a commercial robotaxi deployment
  1. 2.1Cameras and their calibration
  2. 2.2Millimetre-wave radar
  3. 2.3Mechanical and solid-state lidar
  4. 2.4Ultrasonic sensors
  5. 2.5GNSS and inertial systems
  6. 2.6High-definition maps
  7. 2.7Sensor cleaning and degradation
  8. 2.8Adverse weather conditions
  9. 2.9Selecting the sensor suite
  10. 2.10Lab: camera–lidar calibration
  1. 3.1Object detection with deep learning
  2. 3.2Semantic image segmentation
  3. 3.3Point cloud perception
  4. 3.4Early and late sensor fusion
  5. 3.5Multi-object tracking
  6. 3.6Depth estimation
  7. 3.7Lane and sign detection
  8. 3.8Public datasets
  9. 3.9Perception metrics
  10. 3.10Practical exercise: object detection with a YOLO model
  1. 4.1GNSS-RTK localisation
  2. 4.2GNSS-IMU fusion with Kalman filters
  3. 4.3Visual and lidar SLAM
  4. 4.4Localisation in HD maps
  5. 4.5Map maintenance
  6. 4.6Map formats
  7. 4.7Required position accuracy
  8. 4.8Localisation integrity
  9. 4.9Failures and degradation
  10. 4.10Lab: localisation with an extended Kalman filter
  1. 5.1Agent trajectory prediction
  2. 5.2Route planning
  3. 5.3Behaviour planning
  4. 5.4Motion planning
  5. 5.5Trajectory optimisation
  6. 5.6Model predictive control
  7. 5.7Interaction with other road users
  8. 5.8End-to-end planning
  9. 5.9Comfort and safety constraints
  10. 5.10Practical exercise: lattice trajectory planner in Python
  1. 6.1Vehicle dynamic models
  2. 6.2Lateral vehicle control
  3. 6.3Longitudinal speed control
  4. 6.4Drive-by-wire
  5. 6.5Actuator redundancy
  6. 6.6Stability and grip limits
  7. 6.7Controller tuning
  8. 6.8In-vehicle validation
  9. 6.9Integration with ADAS
  10. 6.10Lab: lateral control with Stanley and Pure Pursuit
  1. 7.1Autonomous emergency braking
  2. 7.2Adaptive cruise control
  3. 7.3Lane keeping assist
  4. 7.4Lane change assist
  5. 7.5Traffic sign recognition
  6. 7.6Traffic jam assist
  7. 7.7Remote automated parking
  8. 7.8Driver monitoring
  9. 7.9Euro NCAP assessment
  10. 7.10Case study: functional specification of a motorway assistant
  1. 8.1Centralised and zonal architectures
  2. 8.2High-performance computers
  3. 8.3ROS 2 and AUTOSAR Adaptive middleware
  4. 8.4Redundancy and graceful degradation
  5. 8.5In-vehicle communications
  6. 8.6Software-defined vehicle
  7. 8.7Thermal management and power consumption
  8. 8.8Hardware and software integration
  9. 8.9Software updates
  10. 8.10Practical exercise: functional architecture of a Level 3 system
  1. 9.1ISO 26262 functional safety
  2. 9.2ISO 21448 SOTIF
  3. 9.3Scenario-based validation
  4. 9.4Large-scale simulation
  5. 9.5Track testing
  6. 9.6Public road testing
  7. 9.7Safety metrics
  8. 9.8System safety case
  9. 9.9UL 4600 standard
  10. 9.10Lab: scenario validation in CARLA
  1. 10.1Driver–system interaction
  2. 10.2Transition of control
  3. 10.3Driver situational awareness
  4. 10.4Overreliance
  5. 10.5Driver monitoring
  6. 10.6Communication with pedestrians
  7. 10.7External human–machine interfaces
  8. 10.8Acceptance and trust
  9. 10.9User training
  10. 10.10Workshop: designing a transition-of-control interface
  1. 11.1UNECE Regulation R157 ALKS
  2. 11.2Regulation (EU) 2022/1426 for automated driving systems
  3. 11.3Spanish testing regulations
  4. 11.4Regulation in Germany, the United Kingdom and the US
  5. 11.5Civil liability and insurance
  6. 11.6Data protection
  7. 11.7Ethics of automated driving
  8. 11.8Mandatory cybersecurity under R155
  9. 11.9Expected regulatory developments
  10. 11.10Case study: comparative analysis of regulatory frameworks
  1. 12.1Defining the function and ODD
  2. 12.2System architecture
  3. 12.3Perception and localisation
  4. 12.4Planning and control
  5. 12.5Safety analysis
  6. 12.6Validation in simulation
  7. 12.7Regulatory fit of the function
  8. 12.8Conclusions and future work
  9. 12.9Thesis report
  10. 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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