Master’s in Autonomy, Teleoperation & Perception Engineering
Online master's programme of 1,900 hours in autonomy, teleoperation and perception engineering for off-highway machinery, with 12 modules covering perception sensors, computer vision, localisation and mapping, planning and control, teleoperation, industrial communications, ROS 2, autonomous-system safety (ISO 17757, ISO 18497, ISO 13849-1) and validation, with an assigned technical tutor.
Master’s in Autonomy, Teleoperation & Perception Engineering — studying from the United States
Off-road and specialty vehicle engineering is a strong part of American manufacturing, with heavy equipment and agricultural machinery produced across Illinois, Wisconsin, Iowa, Texas and North Carolina. Companies such as Caterpillar, Deere & Company and Oshkosh Corporation design construction, agricultural, mining and specialty vehicles. Engineers focus on hydraulics, powertrains, operator ergonomics and durability under demanding conditions. Compliance with emissions and safety standards shapes much of the design process.
Fees for the United States: US$8,850 (US Dollars), with instalments and employer sponsorship available.
Relevant bodies in the United States
Typical roles
Questions from the United States
What US bodies are relevant to off-road and special vehicle engineering?
SAE International is central, publishing off-road and heavy machinery standards. Machinery safety is addressed by OSHA, and emissions by the EPA. Professional PE licensure is granted by state boards. SEIUM's qualifications are private and not accredited, so recognition for particular roles is decided by employers or the relevant body.
Where do off-road and special vehicle engineers work in the United States?
Employers include agricultural, construction, and mining equipment manufacturers, defense and specialty vehicle builders, and off-road accessory companies. Roles include design, testing, and drivetrain engineering. Employers evaluate applied skill and experience, so use your final project to show practical competence to hiring managers.
How flexible is the programme for working professionals in the US?
The programmes are 100% online and taught in English, so you can study from home without a student visa. Workloads are 300, 800, or 1,900 hours depending on the programme type, so plan your time accordingly. Fees are payable in instalments, and employer sponsorship may be possible, so ask your employer early.
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
Describe the architecture of off-highway autonomous systems.
Select and calibrate perception sensors.
Develop perception, localisation and control algorithms.
Design safe teleoperation systems.
Plan industrial communication networks.
Apply the safety standards for autonomous machines.
Validate autonomous systems through simulation and field testing.
Who it's for
- Off-highway autonomy engineers working on mining, agricultural or construction machines.
- Perception engineers developing detection and sensor-fusion pipelines.
- Localisation and mapping engineers working with GNSS, odometry and SLAM.
- Teleoperation engineers designing remote-operation stations and links.
- Safety engineers responsible for autonomous systems and their validation.
- Robotics software engineers building on ROS 2 and embedded hardware.
- Engineering or science graduates looking to enter the mining sector.
What you'll work with
- ISO 17757 (safety of autonomous machines in mining)
- ISO 18497 (safety of autonomous agricultural machines)
- UNE-EN ISO 13849-1 (safety of control systems)
- ISO 21448 (safety of the intended functionality)
- ISO 25119 (functional safety in agricultural machinery)
- IEC 62443 (industrial cybersecurity)
- ROS 2
- Gazebo
- MATLAB/Simulink
- PyTorch
- NVIDIA Isaac Sim
12 modules · 1900 h
1.1Levels of machine automation1.2Cases in mining, agriculture and construction1.3Architecture of an autonomous system1.4Perception, localisation and control chain1.5Teleoperation as a transition stage1.6Economic factors of automation1.7Human factors and acceptance1.8Applicable regulatory framework1.9Open technical challenges1.10Case study: automation roadmap for a quarry
2.1LiDAR for work environments2.2Millimetre-wave radar2.3Vision cameras2.4Thermal cameras for detecting people2.5Ultrasonic sensors2.6Inertial measurement units2.7High-precision GNSS receivers2.8Sensor calibration2.9Principles of sensor fusion2.10Lab: camera-LiDAR calibration
3.1Object detection with neural networks3.2Semantic segmentation of scenes3.3Point cloud processing3.4Terrain classification3.5Effect of dust and rain3.6Training datasets3.7Data labelling3.8Performance metrics3.9Deployment on embedded hardware3.10Practical: detecting people with neural networks
4.1GNSS RTK positioning4.2Wheel and visual odometry4.3LiDAR SLAM4.4Work-site maps4.5Extended Kalman filter fusion4.6Operation without GNSS coverage4.7Accuracy and integrity4.8Localisation tools4.9Map maintenance4.10Lab: SLAM with ROS 2
5.1Global route planning5.2Local planning and obstacle avoidance5.3Trajectory generation5.4Trajectory-tracking control5.5Model predictive control5.6Kinematic constraints of the machine5.7Planning of work tasks5.8Coordination between machines5.9Control simulation5.10Practical: trajectory tracking with pure pursuit
6.1Teleoperation architectures6.2Communication requirements6.3Latency and its effect on the operator6.4Video transmission6.5Remote station interfaces6.6Haptic feedback to the operator6.7Safety in teleoperation6.8Operator selection and training6.9Application cases6.10Case study: teleoperation station for an excavator
7.1Private LTE and 5G networks7.2Industrial Wi-Fi networks7.3Radio mesh networks7.4Latency and reliability7.5Coverage in mines and construction sites7.6Network cybersecurity7.7Quality of service7.8Network design7.9Coverage testing7.10Practical: private network design for a mine
8.1ROS 2 as a platform8.2Communication via DDS8.3Software architecture8.4Real-time requirements8.5Containers and deployment8.6Simulation integrated with the software8.7Continuous integration and deployment8.8Software quality8.9Open-source licences8.10Lab: ROS 2 perception node
9.1ISO 17757 requirements in mining9.2ISO 18497 requirements in agriculture9.3Functional safety to ISO 138499.4Safety of the intended functionality9.5Risk analysis of autonomy9.6Exclusion zones and access control9.7Safety validation9.8Lessons from incidents9.9Protection of people in the surroundings9.10Case study: risks of an autonomous tractor
10.1Off-road environment simulators10.2Digital twins of the site10.3Scenario generation10.4Synthetic data generation10.5Hardware-in-the-loop testing of autonomy10.6Field testing10.7Validation metrics10.8Scenario coverage10.9Validation reports10.10Practical: scenarios in a simulator
11.1Organisational change management11.2Staff training11.3Day-to-day operation of the system11.4Maintenance of sensors and systems11.5Performance indicators11.6Deployment costs11.7Deployment cases11.8Labour relations and trade unions11.9Future evolution of autonomy11.10Case study: deployment of autonomous dump trucks
12.1Definition of machine and operation12.2Autonomy requirements12.3Sensor architecture12.4Localisation and planning12.5Teleoperation or supervision12.6Safety analysis12.7Validation plan12.8Economic feasibility of the proposal12.9Thesis report12.10Master's Thesis: defence before a panel
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 you can combine it with your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is ROS 2, Gazebo, MATLAB/Simulink, PyTorch and NVIDIA Isaac Sim, in free, academic or evaluation versions. Testing is studied through real procedures for perception sensor calibration, teleoperation trials, and validation in the simulator and in the field. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: ISO 17757, ISO 18497, UNE-EN ISO 13849-1, ISO 21448, ISO 25119, IEC 62443.
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
- 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.
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 ROS 2, Gazebo, MATLAB/Simulink, PyTorch and NVIDIA Isaac Sim, 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), a computer able to run the programme 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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