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VeVuSafety

Financiado

Artificial Intelligence for Traffic Safety between Vehicles and Vulnerable Road...

Artificial Intelligence for Traffic Safety between Vehicles and Vulnerable Road Users 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 h... ver más

30/09/2024

UNIVERSITEIT TWENT...

188K€

Presupuesto del proyecto: 188K€

Líder del proyecto

UNIVERSITEIT TWENTE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-09-30

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021 ExpectedOutcome:

I+D

Cerrada hace 3 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UNIVERSITEIT TWENTE

187.62K€ | Lider

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Duración del proyecto: 27 meses Fecha Inicio: 2022-06-22
Fecha Fin: 2024-09-30

Líder del proyecto

UNIVERSITEIT TWENTE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 188K€

Descripción del proyecto 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.

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