Skip to main content
European Commission logo

Artificial Intelligence for Traffic Safety between Vehicles and Vulnerable Road Users

European Union
Geo-spatial type
Total project cost
EU Contribution
€187 624
Project Acronym
STRIA Roadmaps
Network and traffic management systems (NTM)
Transport mode
Road icon
Transport policies
Transport sectors
Passenger transport,
Freight transport


Call for proposal
Link to CORDIS
Background & Policy context

In big cities, busy intersections and shared spaces pose a high risk to vulnerable road users like pedestrians and cyclists. Their protection is a challenge for traffic safety officials. However, advances in AI are paving the way for a solution. The EU-funded VeVuSafety project will harness AI methodologies to create a privacy-aware deep learning framework that learns road users’ behaviour in various mixed traffic situations. Advancing a 3D environment model is the next step. Also, an end-to-end deep learning framework using camera data will be built on this environment model for multimodal trajectory prediction, anomaly detection and potential risk classification.


Traffic safety is the fundamental criterion for vehicular environments and many artificial intelligence-based systems like self-driving cars. There are places, e.g., intersections and shared spaces, in the urban environment with high risks where vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists directly interact with each other. By advancing starte-of-the-art artificial intelligence methodologies, this project VeVuSafety aims to build a privacy-aware deep learning framework to learn road users’ behaviour in various mixed traffic situations for the safety between vehicles and VRUs.


 VeVuSafety proposes a 3D environment model based on 3D point cloud for privacy protection — private information like license plates and face is anonymized. Then, within this environment model, an end-to-end deep learning framework using camera data will be built for multimodal trajectory prediction, anomaly detection, and potential risk classification based on deep generative models such as Variational Auto-Encoder. Additionally, an active privacy mechanism will also be adopted by application of the differential privacy mechanism to help the deep learning models prevent model-inversion attack. Moreover, the framework’s generalizability will be investigated by exploring the Normalizing Flows approach for domain adaption. The framework’s performance will be validated at different intersections and shared spaces using real-world traffic data. Besides road user safety and privacy, VeVuSafety can help traffic engineers and city planners to better estimate the design of traffic facilities in order to achieve a road-user-friendly urban traffic environment. Furthermore, the success of VeVuSafety will enhance the fellow’s scientific knowledge and project management skills to become an artificial intelligence expert for traffic safety and Intelligent Transportation Systems.


Specific funding programme
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
Other Programme
HORIZON-MSCA-2021-PF-01-01 MSCA Postdoctoral Fellowships 2021


Lead Organisation
Universiteit Twente
Drienerlolaan 5, 7522 NB Enschede, Netherlands
EU Contribution
€187 624


Technology Theme
Safety systems
Road safety information system
Development phase

Contribute! Submit your project

Do you wish to submit a project or a programme? Head over to the Contribute page, login and follow the process!