Diploma in High-Speed Perception and Fast Lane Decision Making
Online postgraduate diploma of 800 hours in high-speed perception and motorway decision-making. 8 modules covering perception requirements, long-range sensors, high-speed detection and tracking, behaviour prediction, tactical decision-making, critical situations and perception validation, with ISO 21448:2022, ISO/PAS 8800:2024 and UNECE R157, with an assigned technical tutor.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Derive perception requirements for high-speed driving.
Select long-range sensors and their configuration.
Implement low-latency detection and tracking.
Predict the behaviour of other vehicles on the motorway.
Design safe tactical decisions on motorways.
Manage critical situations and sensor degradation.
Validate perception in line with SOTIF and ISO/PAS 8800.
Who it's for
- Perception engineers working on motorway automation.
- Sensor fusion engineers combining radar, lidar and camera data.
- Behaviour prediction engineers modelling surrounding traffic.
- Planning and decision engineers developing tactical driving logic.
- ADAS validation engineers testing long-range perception.
- Radar and lidar specialists moving into automated driving.
- Engineering or science graduates looking to enter vehicle manufacturing.
What you'll work with
- ISO 21448:2022 (safety of the intended functionality, SOTIF)
- ISO/PAS 8800:2024 (safety and artificial intelligence in road vehicles)
- UNECE R157 (Automated Lane Keeping Systems)
- ISO 26262:2018 (road vehicle functional safety)
- ISO 34502:2022 (scenario-based safety evaluation)
- ISO 23793-1 (minimal risk manoeuvre for automated driving)
- IEC 60825-1 (laser product safety)
- ISO 17387 (lane change decision aid systems)
- ROS 2
- PyTorch
- MATLAB Sensor Fusion and Tracking Toolbox
- CARLA
- IPG CarMaker
8 modules
1.1Braking distances at 130 km/h1.2Detection range required per function1.3End-to-end latency budget1.4Angular resolution at long range1.5Front, side and rear field of view1.6Small objects and debris on the road1.7Stationary vehicles at the end of a queue1.8Requirements derived from UNECE R1571.9Allocating requirements to sensors1.10Case study: calculating the minimum detection range
2.177 GHz long-range radar2.24D imaging radar and elevation resolution2.3Long-range lidar at 905 and 1550 nm2.4Telephoto and high-resolution cameras2.5High dynamic range cameras and glare2.6Lidar eye safety according to IEC 60825-12.7Mounting, vibration and online calibration2.8Sensor cleaning and heating2.9Degradation in heavy rain and spray2.10Laboratory: range analysis of a radar with real data
3.1Detecting distant objects with few pixels or points3.2Early, intermediate and late fusion3.3Extended and unscented Kalman filters3.4Multi-object tracking with data association3.5Ego-motion compensation3.6Relative velocity estimation with Doppler radar3.7Handling false positives from fixed structures3.8Long-range lane detection3.9Pipeline latency and optimisation3.10Practical exercise: multi-object tracker with radar–camera fusion
4.1Trajectory prediction on motorways4.2Physics-based versus learned models4.3Early detection of lane change intent4.4Cut-in and cut-out prediction4.5Vehicle interaction at merges4.6Multimodal prediction uncertainty4.7The highD and exiD datasets4.8ADE and FDE metrics4.9Validation in critical scenarios4.10Workshop: cut-in predictor with highD data
5.1Decision architectures: rules, state machines and learning5.2Lane selection and overtaking5.3Handling merges and exits5.4RSS safety models and their formalisation5.5Dynamic safety distances5.6Decision-making under uncertainty with POMDPs5.7Courtesy and cooperative behaviour5.8Compliance with traffic rules5.9Explainability of decisions5.10Laboratory: tactical decision state machine
6.1Emergency braking at high speed6.2Evasive steering versus braking6.3Partial sensor failure while driving6.4Speed reduction due to degraded perception6.5Roadworks and temporary diversions6.6Emergency vehicles and their detection6.7Crosswind, aquaplaning and low grip6.8Tunnels and loss of GNSS6.9Transition to a minimal risk manoeuvre6.10Case study: degradation in heavy rain at 120 km/h
7.1Perception insufficiencies according to ISO 214487.2AI requirements according to ISO/PAS 88007.3Safety-relevant perception metrics7.4Long-range validation on the test track7.5Road data and fleet campaigns7.6High-speed sensor simulation7.7ISO 34502 scenarios for motorways7.8Perception safety argument7.9In-service monitoring7.10Practical exercise: validation plan for detection at 200 metres
8.1Definition of the function and speeds8.2Perception requirements8.3Sensor configuration8.4Detection and tracking pipeline8.5Prediction module8.6Tactical decision module8.7Degradation strategy8.8Validation plan8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
The programme is delivered 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 each intake, so it fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. You work with ROS 2, PyTorch, MATLAB Sensor Fusion and Tracking Toolbox, CARLA and IPG CarMaker, in free, academic or evaluation versions. Testing is studied through real procedures: long-range detection tests on the test track, prediction analysis with naturalistic motorway data, and degradation validation with artificial rain. Assessment is continuous by module, plus the final project.
Standards covered: ISO 21448:2022, ISO/PAS 8800:2024, UNECE R157, ISO 26262:2018, ISO 34502:2022, ISO 23793-1, IEC 60825-1, ISO 17387.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. A university degree is not required.
- 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.
Frequently asked questions
The programme totals 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with ROS 2, PyTorch, MATLAB Sensor Fusion and Tracking Toolbox, CARLA 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), 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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