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Master'sAutonomous, ADAS & Software-Defined Vehicle Applications open

Master’s in Vulnerable Road User Detection and Urban Safety Engineering

Online master's programme of 1,900 hours in vulnerable road user detection and urban safety, structured in 12 modules covering sensing, detection and intention prediction of pedestrians and cyclists, protective ADAS functions, heavy vehicles, cooperative infrastructure, dummy-based validation, SOTIF 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

Analyse vulnerable road user casualties in urban environments.

02

Select sensors for vulnerable road user detection.

03

Develop detection and classification models for pedestrians and cyclists.

04

Predict the intention and trajectory of vulnerable road users.

05

Design protective ADAS functions.

06

Validate systems with dummies and simulation.

07

Develop a vulnerable road user protection project.

Profile

Who it's for

  • Active safety engineers.
  • Urban perception engineers.
  • ADAS validation engineers.
  • Road safety researchers.
  • Protection systems engineers.
  • Urban mobility technicians.
  • Engineering or science graduates looking to enter the automotive sector.
Standards & tools

What you'll work with

  • Regulation (EU) 2019/2144 (General Safety Regulation for motor vehicles)
  • UN ECE R152 (advanced emergency braking systems)
  • UN ECE R151 (blind spot information systems for cyclist detection)
  • UN ECE R159 (moving off information systems)
  • ISO 19206-2 (test targets for vulnerable road users: pedestrians)
  • UN ECE R127 (pedestrian protection)
  • ISO 21448 (safety of the intended functionality, SOTIF)
  • ISO 26262 (functional safety of road vehicles)
  • PyTorch
  • CARLA
  • OpenCV
  • IPG CarMaker
  • ROS 2
Syllabus

12 modules · 1900 h

  1. 1.1Definition of vulnerable road users
  2. 1.2Pedestrian and cyclist casualties
  3. 1.3Vision Zero and the safe system approach
  4. 1.4Critical urban scenarios
  5. 1.5Risk factors at intersections
  6. 1.6Pedestrian crossing behaviour
  7. 1.7Motorcyclists and personal mobility device (PMD) users
  8. 1.8CARE and DGT accident data
  9. 1.9European road safety policies
  10. 1.10Case study: analysis of urban accidents
  1. 2.1High-resolution front cameras
  2. 2.2Radar for pedestrian detection
  3. 2.3LiDAR in urban environments
  4. 2.4Blind-spot cameras
  5. 2.5Short-range side sensors
  6. 2.6Field-of-view coverage
  7. 2.7Occlusions caused by parked vehicles
  8. 2.8Night-time pedestrian detection
  9. 2.9Selecting a sensor suite for the city
  10. 2.10Lab: sensor coverage analysis
  1. 3.1Pedestrian detectors with CNNs
  2. 3.2Detection of cyclists and motorcyclists
  3. 3.3Detection of e-scooter users
  4. 3.4Human pose estimation
  5. 3.5Detection of children and people with reduced mobility
  6. 3.6Detection of partially occluded pedestrians
  7. 3.7CityPersons and EuroCity datasets
  8. 3.8Miss-rate metrics
  9. 3.9Biases in pedestrian datasets
  10. 3.10Practical: cyclist detector with PyTorch
  1. 4.1Crossing-intention cues
  2. 4.2Head and body orientation
  3. 4.3Social force models
  4. 4.4Prediction with recurrent networks
  5. 4.5Prediction with transformers
  6. 4.6Multimodal trajectory prediction
  7. 4.7Interaction between agents
  8. 4.8ADE and FDE metrics
  9. 4.9PIE and JAAD datasets
  10. 4.10Workshop: pedestrian crossing prediction
  1. 5.1Emergency braking for pedestrians
  2. 5.2AEB for cyclists
  3. 5.3Turn assist with detection
  4. 5.4Door-opening warning
  5. 5.5Blind-spot detection in lorries
  6. 5.6Evasive steering assistance
  7. 5.7Active bonnet protection systems
  8. 5.8External pedestrian airbags
  9. 5.9AVAS acoustic warnings
  10. 5.10Practical: AEB logic for pedestrians
  1. 6.1Blind spots of lorries and buses
  2. 6.2Regulation R151 on cyclist detection
  3. 6.3Regulation R159 on moving-off information systems
  4. 6.4Direct vision systems
  5. 6.5London's Direct Vision Standard
  6. 6.6Camera mirrors and monitors
  7. 6.7Side radar on lorries
  8. 6.8Training for professional drivers
  9. 6.9Effectiveness assessment
  10. 6.10Case study: blind-spot system on a lorry
  1. 7.1Pedestrian detection from infrastructure
  2. 7.2Smart intersections with sensors
  3. 7.3VAM messages for vulnerable road users
  4. 7.4Cooperative warnings to vehicles
  5. 7.5Adaptive traffic lights with pedestrian detection
  6. 7.6Smart pedestrian crossings
  7. 7.7Infrastructure cameras and privacy
  8. 7.8Integration with traffic management centres
  9. 7.9Urban pilot projects
  10. 7.10Lab: intersection warning system
  1. 8.1Pedestrian and cyclist dummies
  2. 8.2Mobile test platforms
  3. 8.3Euro NCAP vulnerable road user scenarios
  4. 8.4Night-time tests with dummies
  5. 8.5Reconstruction of real accidents
  6. 8.6Simulation of urban scenarios
  7. 8.7Harm-reduction metrics
  8. 8.8Track and city testing
  9. 8.9Validation reports
  10. 8.10Workshop: CPNA scenario in simulation
  1. 9.1HARA of protective functions
  2. 9.2False positives and unwanted braking
  3. 9.3Perception insufficiencies
  4. 9.4Urban triggering conditions
  5. 9.5Trade-off between safety and comfort
  6. 9.6Statistical validation of failure rates
  7. 9.7Field monitoring
  8. 9.8Function updates
  9. 9.9Manufacturer liability
  10. 9.10Case study: SOTIF analysis of pedestrian AEB
  1. 10.1Traffic calming and 30 km/h zones
  2. 10.2Design of safe intersections
  3. 10.3Safe cycling infrastructure
  4. 10.4Urban speed management
  5. 10.5School zones and protected environments
  6. 10.6Open road casualty data
  7. 10.7Evaluation of measures
  8. 10.8Public participation in design
  9. 10.9Integration with automated vehicles
  10. 10.10Practical: urban road safety audit
  1. 11.1General Safety Regulation 2019/2144
  2. 11.2Regulation R152 on AEB
  3. 11.3Euro NCAP vulnerable road user protocols
  4. 11.4Pedestrian protection requirements R127
  5. 11.5Regulations R151 and R159
  6. 11.6ISO 19206 standards for test targets
  7. 11.7Road safety directives
  8. 11.8Effectiveness assessment by the EU
  9. 11.9Regulatory trends towards 2030
  10. 11.10Workshop: AEB compliance matrix
  1. 12.1Selection of critical scenarios
  2. 12.2Definition of the sensor suite
  3. 12.3Development of detection and classification
  4. 12.4Intention prediction model
  5. 12.5Design of the protective function
  6. 12.6Functional safety analysis
  7. 12.7Validation plan with dummies
  8. 12.8Results and conclusions
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

The programme 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 the intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is PyTorch, CARLA, OpenCV, IPG CarMaker and ROS 2, in free, academic or evaluation versions. Testing is studied through real procedures for tests with pedestrian and cyclist dummies, simulation of critical urban scenarios and reconstruction of real accidents. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2019/2144 (General Safety), UN ECE R152, UN ECE R151, UN ECE R159, ISO 19206-2, UN ECE R127, ISO 21448, ISO 26262.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2019/2144, UN ECE R152 and UN ECE R151 applied to real cases, not as a theoretical annex.
Industry software:PyTorch, CARLA, OpenCV, IPG CarMaker and ROS 2, in free, academic or evaluation versions.
Master's Thesis with defence:A vulnerable road user protection system, from critical-scenario selection to a dummy-based validation plan, 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.

Critical scenarios and sensors
Select critical urban scenarios and define the sensor suite.
Detection and classification
Develop pedestrian and cyclist detection and classification models.
Intention prediction
Build a model to predict crossing intention and trajectories.
Protective function design
Design the protective function, such as pedestrian AEB, with its functional safety analysis.
Master's Thesis
Plan validation with dummies and present results and conclusions, defended before a panel.
Career opportunities

Where this takes you

Active Safety EngineerUrban Perception EngineerADAS Validation EngineerRoad Safety ResearcherProtection Systems EngineerUrban Mobility Technician
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme's 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 PyTorch, CARLA, OpenCV, IPG CarMaker and ROS 2, 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 university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the 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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