Description du projet
Prédire les intentions des piétons pour améliorer la sécurité des véhicules intelligents
Les niveaux de sécurité des véhicules intelligents sont très élevés dans des conditions structurées comme les autoroutes, mais le sont beaucoup moins dans des environnements moins structurés comme les carrefours pour piétons et les environnements à trafic mixte. Le projet SSVPI, financé par l’UE, étudiera la prédiction des intentions des piétons, un aspect essentiel à la sécurité des véhicules intelligents. Plus précisément, le projet développera des algorithmes multi-sources et multi-modaux capables de prédire les intentions des piétons dans des conditions d’éclairage difficiles. Les résultats serviront de base à de nombreuses initiatives gouvernementales et commerciales dans le domaine de la conduite autonome.
Objectif
There has been a lot of research on smart vehicles (SV, including autonomous vehicles and smart powered wheelchairs), mainly for motorways and other structured environments, with resulting safety levels in such highly structured conditions being excellent. However, the situation is different for less structured environments, particularly where interaction between SV and pedestrians is possible, such as pedestrian junctions and mixed traffic environments. In these cases, more fundamental research in safety aspects is needed, since even minor contact between humans and vehicle poses serious dangers to unprotected humans. Specifically, pedestrian intention prediction is crucial for safe and smooth SV operation. This project aims to develop multi-source and multi-modal algorithms which can predict intentions of pedestrians under challenging lighting conditions (using both visible (RGB) and thermal imaging), using cues from both pedestrian movements as well as their environmental and social context. The project aims at enhancing the safety level of pedestrians in the context of SV in unstructured environments. Apart from the development of novel algorithms in this challenging domain, we aim to maximise the impact of our research through the creation of one of the first pedestrian intention prediction datasets combining RGB and thermal images. Performance evaluation of intention pedestrian algorithms will involve both vehicles in intersections, as well as smart wheelchairs for people with disabilities. By enhancing the safety level of SV and pedestrians through predicting pedestrians' intention under various lighting conditions, the results of this project will be very helpful for the development of SV, and will also promote the public' s acceptance of SV. Consequently, the results of this project are very beneficial for the EU, where multiple governmental and commercial autonomous driving initiatives are active.
Champ scientifique
CORDIS classe les projets avec EuroSciVoc, une taxonomie multilingue des domaines scientifiques, grâce à un processus semi-automatique basé sur des techniques TLN.
CORDIS classe les projets avec EuroSciVoc, une taxonomie multilingue des domaines scientifiques, grâce à un processus semi-automatique basé sur des techniques TLN.
Mots‑clés
Programme(s)
Régime de financement
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinateur
SW7 2AZ LONDON
Royaume-Uni