Enhancing Motion Prediction by a Cooperative Framework

Javier Araluce*, Alberto Justo, Asier Arizala, Leonardo González, Sergio Díaz

*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Cooperative perception is a technique that enhances the on-board sensing and perception of automated vehicles by fusing data from multiple sources, such as other vehicles, roadside infrastructure, cloud/edge servers, among others. It can improve the performance of automated driving in complex scenarios, like unsignalled roundabouts or intersections where the visibility and awareness of other road users are limited. Motion Prediction (MP) is a key component of cooperative perception, as it enables the estimation and prediction of microscopic traffic states, such as the positions and speeds of all vehicles. It relies on information from other agents and their relationships among them, so the information provided by external sources is valuable because it enhances the understanding of the scene.In this paper, we present improved MP through Vehicle to Vehicle (V2V) communication. We have trained Hierarchical Vector Transformer (HiVT) to be a map-less solution that can be used in road domains. With this model, we have implemented and compared two association methods to evaluate our framework on a real V2V dataset (V2V4Real). Our evaluation concludes that our V2V MP improves performance due to better scene understanding over a single-vehicle MP.

Idioma originalInglés
Título de la publicación alojada35th IEEE Intelligent Vehicles Symposium, IV 2024
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas1389-1394
Número de páginas6
ISBN (versión digital)9798350348811
DOI
EstadoPublicada - 2024
Evento35th IEEE Intelligent Vehicles Symposium, IV 2024 - Jeju Island, República de Corea
Duración: 2 jun 20245 jun 2024

Serie de la publicación

NombreIEEE Intelligent Vehicles Symposium, Proceedings
ISSN (versión impresa)1931-0587
ISSN (versión digital)2642-7214

Conferencia

Conferencia35th IEEE Intelligent Vehicles Symposium, IV 2024
País/TerritorioRepública de Corea
CiudadJeju Island
Período2/06/245/06/24

Huella

Profundice en los temas de investigación de 'Enhancing Motion Prediction by a Cooperative Framework'. En conjunto forman una huella única.

Citar esto