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TRIMIS

Artificial Intelligence for Traffic Safety between Vehicles and Vulnerable Road Users

PROJECTS
Funding
European
European Union
Duration
-
Geo-spatial type
Urban
Total project cost
€0
EU Contribution
€187 624
Project Acronym
VeVuSafety
STRIA Roadmaps
Network and traffic management systems (NTM)
Transport mode
Road icon
Transport policies
Safety/Security
Transport sectors
Passenger transport,
Freight transport

Overview

Call for proposal
HORIZON-MSCA-2021-PF-01
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.

Objectives

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.

Methodology

 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.

Funding

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

Partners

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

Technologies

Technology Theme
Safety systems
Technology
Road safety information system
Development phase
Research/Invention

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