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Master’s in Trajectory and Behaviour Planning Engineering

Online master's programme of 1,900 hours in trajectory and behaviour planning engineering, structured in 12 modules covering behaviour planning, sampling and search, trajectory and speed optimisation, interactive planning, uncertainty and risk, learned planning, planner safety, special scenarios 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 tactical behaviour planning layers.

02

Implement sampling-based and optimisation-based planners.

03

Plan safe and comfortable speed profiles.

04

Incorporate interaction and uncertainty into planning.

05

Apply learned methods to planning.

06

Verify the safety of the planner.

07

Develop a complete trajectory planner.

Profile

Who it's for

  • Trajectory planning engineers.
  • Behaviour engineers.
  • Optimisation engineers.
  • Autonomous driving safety engineers.
  • Planning researchers.
  • Validation engineers.
  • Engineering or science graduates looking to enter the automotive sector.
Standards & tools

What you'll work with

  • ISO 21448 (safety of the intended functionality, SOTIF)
  • ISO 26262 (functional safety of road vehicles)
  • IEEE 2846 (assumptions for models in automated driving safety)
  • UN ECE R157 (Automated Lane Keeping Systems, ALKS)
  • ISO 34502 (scenario-based safety evaluation framework)
  • ASAM OpenSCENARIO (driving scenario description format)
  • SAE J3016 (levels of driving automation)
  • ISO 34503 (taxonomy for the operational design domain)
  • CommonRoad
  • Autoware
  • CARLA
  • OSQP
  • ROS 2
Syllabus

12 modules · 1900 h

  1. 1.1The trajectory planning problem
  2. 1.2Hierarchical behaviour planning
  3. 1.3Traffic and dynamic constraints
  4. 1.4Cost functions and objectives
  5. 1.5Planning horizons
  6. 1.6Real time and frequency
  7. 1.7Interaction with prediction
  8. 1.8Evaluation of planners
  9. 1.9Industrial state of the art
  10. 1.10Case study: comparison of the Apollo and Autoware planners
  1. 2.1Tactical behaviour layer
  2. 2.2Hierarchical state machines
  3. 2.3Behaviour trees
  4. 2.4Traffic rules as constraints
  5. 2.5Lane-change decision
  6. 2.6Handling intersections and roundabouts
  7. 2.7Motorway merging
  8. 2.8Behaviour towards emergency vehicles
  9. 2.9Social behaviour and courtesy
  10. 2.10Lab: lane-change logic
  1. 3.1Hybrid A* algorithms
  2. 3.2Planning on state lattices
  3. 3.3Kinodynamic RRT and RRT*
  4. 3.4Planning in Frenet coordinates
  5. 3.5Sampling of polynomial trajectories
  6. 3.6Space-time search
  7. 3.7Heuristics and pruning
  8. 3.8Computational complexity and real time
  9. 3.9Comparison of sampling methods
  10. 3.10Practical: hybrid A* for parking
  1. 4.1Formulation as a non-linear programme
  2. 4.2Quadratic programming (QP)
  3. 4.3Direct collocation methods
  4. 4.4Piecewise convex optimisation
  5. 4.5Trajectory smoothing
  6. 4.6Collision constraints
  7. 4.7OSQP and IPOPT solvers
  8. 4.8Initialisation and convergence
  9. 4.9Real-time optimisation
  10. 4.10Workshop: trajectory smoothing with OSQP
  1. 5.1ST speed profiles
  2. 5.2Space-time graphs of obstacles
  3. 5.3Dynamic programming for speed
  4. 5.4Speed optimisation with QP
  5. 5.5Comfort constraints
  6. 5.6Interaction with lead vehicles
  7. 5.7Handling traffic lights
  8. 5.8Speed in bends
  9. 5.9Energy-efficient planning
  10. 5.10Lab: ST planner with QP
  1. 6.1Interaction with other agents
  2. 6.2Prediction-conditioned planning
  3. 6.3Game theory in driving
  4. 6.4POMDP models for interaction
  5. 6.5Contingency planning
  6. 6.6Negotiation when merging
  7. 6.7Assertive versus conservative behaviour
  8. 6.8Human driver models
  9. 6.9Interaction evaluation
  10. 6.10Practical: merging with game theory
  1. 7.1Sources of uncertainty
  2. 7.2Probabilistic planning with predictions
  3. 7.3Chance constraints
  4. 7.4Risk fields
  5. 7.5Occupancy-based planning
  6. 7.6Hidden zones and occlusions
  7. 7.7Robust planning against disturbances
  8. 7.8Risk metrics
  9. 7.9Calibrating conservativeness
  10. 7.10Workshop: planning with hidden zones
  1. 8.1Imitation learning of trajectories
  2. 8.2Neural trajectory planners
  3. 8.3Learned cost functions
  4. 8.4Inverse reinforcement learning
  5. 8.5Diffusion models for trajectories
  6. 8.6Planning with transformers
  7. 8.7Hybrid learned-classical planning
  8. 8.8Closed-loop evaluation
  9. 8.9Generalisation and rare cases
  10. 8.10Lab: planner in nuPlan
  1. 9.1RSS safety model
  2. 9.2Independent safety checks
  3. 9.3Minimal risk manoeuvres
  4. 9.4Fallback trajectories
  5. 9.5Formal verification of manoeuvres
  6. 9.6Set reachability
  7. 9.7Functional safety requirements
  8. 9.8SOTIF in behaviour
  9. 9.9Safety argumentation
  10. 9.10Case study: verification of a lane change
  1. 10.1Automated perpendicular parking
  2. 10.2Roadworks zones
  3. 10.3Environments without road markings
  4. 10.4Single-lane roads
  5. 10.5Interaction with pedestrians
  6. 10.6Rural roads and environments
  7. 10.7Driving in traffic jams
  8. 10.8Adverse weather conditions
  9. 10.9Emergency situations
  10. 10.10Practical: planning in a roadworks zone
  1. 11.1Planner safety metrics
  2. 11.2Comfort and progress metrics
  3. 11.3nuPlan and CommonRoad benchmarks
  4. 11.4Closed-loop simulation
  5. 11.5Generated critical scenarios
  6. 11.6Failure analysis
  7. 11.7Track testing
  8. 11.8Regression testing and CI
  9. 11.9Evaluation reports
  10. 11.10Workshop: evaluation in CommonRoad
  1. 12.1Definition of the ODD and scenarios
  2. 12.2Design of the behaviour layer
  3. 12.3Design of the trajectory planner
  4. 12.4Speed planning
  5. 12.5Uncertainty management
  6. 12.6Planner safety
  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 CommonRoad, Autoware, CARLA, OSQP and ROS 2, in free, academic or evaluation versions. Testing is studied through real procedures for evaluation on planning benchmarks, closed-loop simulation of critical scenarios and track tests of manoeuvres. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 21448, ISO 26262, IEEE 2846, UN ECE R157, ISO 34502, ASAM OpenSCENARIO, SAE J3016, ISO 34503.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 21448, ISO 26262 and IEEE 2846 applied to real cases, not as a theoretical annex.
Industry software:CommonRoad, Autoware, CARLA, OSQP and ROS 2, in free, academic or evaluation versions.
Master's Thesis with defence:A complete trajectory and behaviour planner for a defined ODD, 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.

ODD and scenarios
Define the ODD and the target scenarios.
Behaviour layer
Design the tactical behaviour layer with state machines or behaviour trees.
Trajectory and speed planning
Design the trajectory planner and the speed planning.
Uncertainty and safety
Handle uncertainty and occlusions and add independent safety checks.
Master's Thesis
Validate the planner in simulation and present conclusions, defended before a panel.
Career opportunities

Where this takes you

Trajectory Planning EngineerBehaviour EngineerOptimisation EngineerAutonomous Driving Safety EngineerPlanning ResearcherValidation Engineer
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 CommonRoad, Autoware, CARLA, OSQP 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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