Postgraduate Diploma in Edge/VEC and Cooperative Perception
Online 800 h Postgraduate Diploma in Edge/VEC and Cooperative Perception. 8 modules with ETSI TS 103 324, SAE J3224 and CARLA. Assigned tutor and enrolment open.
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What you will be able to do
Understand the architecture of C-ITS systems and the ITS-G5 and C-V2X technologies.
Design vehicular edge computing services with bounded latency requirements.
Apply on-board sensor fusion techniques with explicit uncertainty.
Implement the collective perception service according to ETSI TS 103 324.
Evaluate cooperative fusion architectures based on deep learning.
Analyse the security, privacy and trust requirements in V2X.
Validate cooperative systems through co-simulation and objective metrics.
Who it's for
- V2X systems engineer who wants to specialise in Edge/VEC and Cooperative Perception.
- Perception engineer for autonomous vehicles who wants to specialise in Edge/VEC and Cooperative Perception.
- Edge computing architect who wants to specialise in Edge/VEC and Cooperative Perception.
- C-ITS integration engineer who wants to specialise in Edge/VEC and Cooperative Perception.
- Sensor fusion specialist who wants to specialise in Edge/VEC and Cooperative Perception.
- Automated driving validation engineer who wants to specialise in Edge/VEC and Cooperative Perception.
- Engineering or science graduates looking to enter the automotive sector.
What you'll work with
- ETSI TS 103 324
- SAE J3224
- ETSI EN 302 637-2
- ETSI EN 302 637-3
- ETSI GS MEC 030
- 3GPP TS 23.287
- ISO/SAE 21434
- ISO 26262
8 modules · 800 h
1.1Architecture of C-ITS cooperative ITS systems1.2ETSI ITS-G5 and C-V2X protocol stack1.3CAM and DENM messages: content and frequencies1.4PC5 and Uu modes in LTE-V2X and NR-V2X1.5On-board units (OBU) and roadside units (RSU)1.6End-to-end latency in vehicular services1.7Mobility and traffic density models1.8Cooperative driving use cases1.9Reliability requirements per 3GPP TS 22.1861.10Practical: capturing and decoding CAM messages
2.1ETSI MEC architecture and application platform2.2V2X Information Service per ETSI GS MEC 0302.3Offloading computation from vehicle to edge2.4Task scheduling under deadline constraints2.5Service migration during cell handover2.6Container orchestration on roadside nodes2.7Network slicing for critical vehicular traffic2.8Energy–latency cost models in offloading2.9HD map caching at the edge2.10Laboratory: deploying a MEC app with K3s
3.1Camera, LiDAR and radar: comparative characteristics3.2Multisensor extrinsic and intrinsic calibration3.33D object detection in point clouds3.4Multi-object tracking with Kalman filters3.5Data association and the Hungarian algorithm3.6Occupancy grid representations3.7Uncertainty and covariance in detections3.8Temporal synchronisation of sensors with PTP3.9nuScenes and KITTI datasets3.10Workshop: LiDAR–camera fusion with ROS 2
4.1CPM message according to ETSI TS 103 3244.2Perceived object and free-space containers4.3Message generation rules and redundancy4.4Sensor sharing profiles in SAE J32244.5Coordinate transformations between transmitters4.6Distributed fusion of received objects4.7Covariance intersection and conservative fusion4.8Channel load and DCC congestion control4.9Extended environmental awareness metrics4.10Practical: CPM generation in SUMO simulation
5.1Early, intermediate and late fusion5.2Feature sharing in BEV view5.3V2X-ViT and CoBEVT networks: principles5.4Feature map compression5.5Robustness to pose errors and latency5.6Federated learning across fleets5.7OPV2V and V2X-Sim datasets5.8Deploying inference on edge GPUs5.9Quantisation and optimisation with TensorRT5.10Laboratory: cooperative training on OPV2V
6.1European C-ITS public key infrastructure6.2Authorisation certificates and pseudonyms6.3ECDSA signing and real-time verification6.4Detection of misbehaviour and false data6.5Spoofing and replay attacks6.6Trajectory privacy and identity rotation6.7Automotive cybersecurity per ISO/SAE 214346.8Functional safety and ISO 26262 in cooperation6.9Responsibility and ethics in shared decisions6.10Case study: detecting a malicious node
7.1Co-simulation with CARLA, SUMO and Veins7.2Vehicular radio channel modelling7.3Accuracy, range and freshness metrics7.4Occlusion and blind-intersection scenarios7.5Testing in digital corridors and pilots7.6Trials with real RSUs in urban environments7.7Sensitivity analysis of market penetration7.8Scenario-based validation using ASAM OpenSCENARIO7.9Preparing evaluation reports7.10Laboratory: intersection scenario with occlusions
8.1Defining the use case and scope8.2Selecting sensors, OBUs and RSUs8.3Designing the system's edge architecture8.4Implementing the CPM message flow8.5Integrating cooperative fusion8.6Test plan and success metrics8.7Running the simulation campaign8.8Analysis of results and limitations8.9Dissertation and presentation8.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetable: students progress at their own pace within the enrolment period, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is CARLA, SUMO, Veins/OMNeT++, ROS 2, Autoware, in free, academic or evaluation versions. Trials are studied using real procedures for simulating occluded intersections, CPM message testing on an urban corridor, and latency measurement on MEC nodes. Assessment is continuous per module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Understand the architecture of C-ITS systems and the ITS-G5 and C-V2X technologies.
Design vehicular edge computing services with bounded latency requirements.
Apply on-board sensor fusion techniques with explicit uncertainty.
Implement the collective perception service according to ETSI TS 103 324.
Evaluate cooperative fusion architectures based on deep learning.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient command of Spanish to follow technical standards documentation.
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 spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
The work is carried out with CARLA, SUMO, Veins/OMNeT++, ROS 2, Autoware. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrative project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area can be credited towards the advanced programme. Please check the credit transfer policy when you enrol.
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