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

Master’s in Planning, Decision & Control Engineering

Online master's programme of 1,900 hours in planning, decision-making and control engineering for automated driving, structured in 12 modules covering decision and control architecture, route planning, behaviour prediction, decision-making, motion planning, vehicle modelling, lateral and longitudinal control, safety, learning-based planning and validation, 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

Design decision and control architectures for autonomous driving.

02

Implement route and motion planning algorithms.

03

Develop behaviour prediction models.

04

Design safe decision-making logic.

05

Implement lateral and longitudinal controllers.

06

Validate the system through simulation and scenarios.

07

Develop a complete planning and control project.

Profile

Who it's for

  • Motion planning engineers.
  • Vehicle control engineers.
  • Decision-making engineers.
  • Behaviour prediction engineers.
  • Autonomous driving validation engineers.
  • Mobile robotics researchers.
  • Engineering or science graduates looking to enter the automotive sector.
Standards & tools

What you'll work with

  • ISO 26262 (functional safety of road vehicles)
  • ISO 21448 (safety of the intended functionality, SOTIF)
  • UN ECE R157 (Automated Lane Keeping Systems, ALKS)
  • IEEE 2846 (assumptions for models in automated driving safety)
  • ISO 34502 (scenario-based safety evaluation framework)
  • ASAM OpenSCENARIO (driving scenario description format)
  • SAE J3016 (levels of driving automation)
  • ISO 22179 (full speed range adaptive cruise control)
  • Autoware
  • CARLA
  • MATLAB/Simulink
  • ROS 2
  • IPG CarMaker
Syllabus

12 modules · 1900 h

  1. 1.1The autonomous driving stack
  2. 1.2Mission, behaviour and motion hierarchy
  3. 1.3Modular versus end-to-end architectures
  4. 1.4Interfaces with perception and prediction
  5. 1.5Environment representation for planning
  6. 1.6Real-time requirements
  7. 1.7Vehicle models for control
  8. 1.8Middleware and execution
  9. 1.9Apollo and Autoware examples
  10. 1.10Case study: analysis of the Autoware stack
  1. 2.1Road network graphs
  2. 2.2Dijkstra and A* algorithms
  3. 2.3Contraction hierarchies for routing
  4. 2.4Routing with ODD constraints
  5. 2.5Routing by energy and traffic
  6. 2.6Dynamic replanning in response to incidents
  7. 2.7Integration with HD maps
  8. 2.8Lane-level planning
  9. 2.9Route evaluation
  10. 2.10Lab: A* planning on Lanelet2
  1. 3.1Physics-based prediction
  2. 3.2Manoeuvre-based prediction
  3. 3.3Prediction with deep networks
  4. 3.4Multimodal trajectory prediction
  5. 3.5Prediction conditioned on the ego action
  6. 3.6Interaction and game theory
  7. 3.7Argoverse and nuScenes datasets
  8. 3.8minADE and miss-rate metrics
  9. 3.9Uncertainty in prediction
  10. 3.10Practical: trajectory predictor with LSTM
  1. 4.1Finite state machines
  2. 4.2Behaviour trees
  3. 4.3Formalised traffic rules
  4. 4.4Decision-making under uncertainty (POMDP)
  5. 4.5Game theory in interaction
  6. 4.6Risk models for decision-making
  7. 4.7Decision-making at intersections
  8. 4.8Lane changes and overtaking
  9. 4.9Behaviour in emergencies
  10. 4.10Workshop: behaviour tree for the motorway
  1. 5.1Configuration space
  2. 5.2Sampling with RRT and RRT*
  3. 5.3State lattices
  4. 5.4Frenet frame and polynomials
  5. 5.5Trajectory optimisation
  6. 5.6Bézier curves and splines
  7. 5.7Dynamic and comfort constraints
  8. 5.8Collision checking
  9. 5.9Planning in car parks
  10. 5.10Lab: Frenet planner in Python
  1. 6.1Kinematic bicycle model
  2. 6.2Linear dynamic model
  3. 6.3Pacejka tyre models
  4. 6.4Longitudinal dynamics and traction
  5. 6.5Lateral dynamics and yaw
  6. 6.6Load transfer
  7. 6.7Parameter identification
  8. 6.8Models for control versus simulation
  9. 6.9Model validation
  10. 6.10Practical: dynamic model identification
  1. 7.1Pure Pursuit control
  2. 7.2Stanley path-tracking controller
  3. 7.3Lateral LQR control
  4. 7.4Lateral predictive control
  5. 7.5Curvature feedforward
  6. 7.6Delay compensation
  7. 7.7Low-speed control
  8. 7.8Robustness to parameters
  9. 7.9Tracking evaluation
  10. 7.10Workshop: comparison of lateral controllers
  1. 8.1Speed and distance control
  2. 8.2Adaptive cruise control
  3. 8.3Torque and braking control
  4. 8.4Stop-and-go management
  5. 8.5Comfort and jerk
  6. 8.6Coordination with the powertrain
  7. 8.7Control on gradients
  8. 8.8Cooperative CACC control
  9. 8.9Comfort evaluation
  10. 8.10Lab: ACC with MPC control
  1. 9.1RSS safety models
  2. 9.2Safety Force Field
  3. 9.3Runtime safety monitors
  4. 9.4Minimal risk manoeuvre
  5. 9.5Formal verification of decisions
  6. 9.6ISO 26262 requirements in control
  7. 9.7SOTIF in planning
  8. 9.8Control redundancy
  9. 9.9Degraded vehicle states
  10. 9.10Case study: applying RSS to a lane change
  1. 10.1Imitation learning
  2. 10.2Reinforcement learning
  3. 10.3Data-driven learned planners
  4. 10.4End-to-end models
  5. 10.5World models
  6. 10.6Planning with transformers
  7. 10.7Closed-loop evaluation
  8. 10.8Combination with classical methods
  9. 10.9Risks and explainability
  10. 10.10Practical: imitation learning in nuPlan
  1. 11.1Closed-loop simulation
  2. 11.2CARLA and CarMaker simulators
  3. 11.3Scenarios in OpenSCENARIO format
  4. 11.4Safety and comfort metrics
  5. 11.5Scenario-based testing
  6. 11.6SIL and HIL testing
  7. 11.7Track testing
  8. 11.8Disengagement analysis
  9. 11.9Validation reports
  10. 11.10Workshop: scenario suite in CARLA
  1. 12.1Definition of the use case
  2. 12.2System architecture
  3. 12.3Design of the prediction
  4. 12.4Design of the decision-making
  5. 12.5Design of the motion planning
  6. 12.6Design of lateral and longitudinal control
  7. 12.7Validation in simulation
  8. 12.8Conclusions and future work
  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 Autoware, CARLA, MATLAB/Simulink, ROS 2 and IPG CarMaker, in free, academic or evaluation versions. Testing is studied through real procedures for closed-loop scenario simulation, control tests on track and evaluation with safety and comfort metrics. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 26262, ISO 21448, UN ECE R157, IEEE 2846, ISO 34502, ASAM OpenSCENARIO, SAE J3016, ISO 22179.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 26262, ISO 21448 and UN ECE R157 applied to real cases, not as a theoretical annex.
Industry software:Autoware, CARLA, MATLAB/Simulink, ROS 2 and IPG CarMaker, in free, academic or evaluation versions.
Master's Thesis with defence:A complete planning, decision-making and control system for a defined use case, validated in simulation, 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.

Use case and architecture
Define the use case and the system architecture.
Prediction and decision-making
Design the behaviour prediction and the decision-making logic.
Motion planning
Design the motion planner, for example in the Frenet frame.
Lateral and longitudinal control
Design the lateral and longitudinal controllers.
Master's Thesis
Validate the system in simulation and present conclusions, defended before a panel.
Career opportunities

Where this takes you

Motion Planning EngineerVehicle Control EngineerDecision-making EngineerBehaviour Prediction EngineerAutonomous Driving Validation EngineerMobile Robotics Researcher
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 Autoware, CARLA, MATLAB/Simulink, ROS 2 and IPG CarMaker, 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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