HEИндивидуална стипендия2022–2024

VeVuSafety · Artificial Intelligence for Traffic Safety between Vehicles and Vulnerable Road Users

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период: 2022-10-01 → 2024-09-30
Финансиране от ЕС: 187 624 €
Участници: 1
Схема: HORIZON-TMA-MSCA-PF-EF

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Накратко на български

Изкуственият интелект анализира поведението и траекториите на пешеходци, колоездачи и автомобили на места като кръстовищата. Това помага за по-безопасно движение и подобрява работата на автономните автомобили в градска среда.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Artificial Intelligence for Traffic Safety between Vehicles and Vulnerable Road Users

Traffic safety is a fundamental criterion in vehicular environments and for many artificial intelligence-based systems, such as autonomous vehicles. Some areas in urban environments, such as intersections and shared spaces, present high risks where vehicles and vulnerable road users (VRUs) directly interact. By advancing state-of-the-art artificial intelligence methodologies, the VeVuSafety project aims to build deep learning frameworks to understand road users' behavior in various mixed traffic situations, ensuring the safety of both vehicles and VRUs. The project objectives include: WP1, mapping the traffic environment with static and dynamic objects, such as road surfaces and road users; WP2, predicting the trajectories of various road users, such as pedestrians, cyclists, and vehicles; WP3, analyzing road users' dynamic behaviors and their interactions; and WP4, facilitating autonomous driving and safer traffic conditions.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

Участници

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