Výsledky vyhledávání - "Sierra Gonzalez, David"

Report

TLCFuse: Temporal Multi-Modality Fusion Towards Occlusion-Aware Semantic Segmentation-Aided Motion Planning

Autor: Salazar-Gomez, Gustavo, Liu, Wenqian, Diaz-Zapata, Manuel, Sierra-Gonzalez, David, Laugier, Christian

In autonomous driving, addressing occlusion scenarios is crucial yet challenging. Robust surrounding perception is essential for handling occlusions and aiding motion planning. State-of-the-art models fuse Lidar and Camera data to produce impressive

Externí odkaz: http://arxiv.org/abs/2311.05319

Zobrazit plný text záznamu

Dissertation/ Thesis

Towards Human-Like Prediction and Decision-Making for Automated Vehicles in Highway Scenarios

Autor: Sierra Gonzalez, David

Au cours des dernières décennies, les constructeurs automobiles ont constamment introduit des innovations technologiques visant à rendre les véhicules plus sûrs. Le niveau de sophistication de ces systèmes avancés d’aide à la conduite s’e

Externí odkaz: http://www.theses.fr/2019GREAM012/document

Zobrazit plný text záznamu

Vers une prédiction et une prise de décision inspirées de celles des humains pour la conduite automatisée de véhicules sur autoroute

Autor: Sierra Gonzalez, David

Publikováno v: Artificial Intelligence [cs.AI]. Université Grenoble Alpes, 2019. English. ⟨NNT : 2019GREAM012⟩

During the past few decades automakers have consistently introduced technological innovations aimed to make road vehicles safer. The level of sophistication of these advanced driver assistance systems has increased parallel to developments in sensor

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::f4587662ffeb95e14475afb74f19ee6b
https://tel.archives-ouvertes.fr/tel-02184362/document

Zobrazit plný text záznamu

Leveraging Dynamic Occupancy Grids for 3D Object Detection in Point Clouds

Autor: Ozgur Erkent, Jilles S. Dibangoye, David Sierra-Gonzalez, Anshul Paigwar, Christian Laugier

Publikováno v: ICARCV 2020-16th IEEE International Conference on Control, Automation, Robotics and Vision
ICARCV 2020-16th IEEE International Conference on Control, Automation, Robotics and Vision, Dec 2020, Shenzhen, China. pp.1-6
ICARCV

International audience; Traditionally, point cloud-based 3D object detectors are trained on annotated, non-sequential samples taken from driving sequences (e.g. the KITTI dataset). However, by doing this, the developed algorithms renounce to exploit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c9f9e3c644b6ef11bf5cf329f38ebb68
https://inria.hal.science/hal-03044979/document

Zobrazit plný text záznamu

Human-Like Decision-Making for Automated Driving in Highways

Autor: Jilles S. Dibangoye, David Sierra Gonzalez, Mario Garzón, Christian Laugier

Publikováno v: ITSC
ITSC 2019-22nd IEEE International Conference on Intelligent Transportation Systems
ITSC 2019-22nd IEEE International Conference on Intelligent Transportation Systems, Oct 2019, Auckland, New Zealand. pp.1-8

In this work, we present a decision-making system for automated vehicles driving in highway environments. The task is modeled as a Partially Observable Markov Decision Process, in which the physical states and intentions of surrounding traffic are un

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c074b80dfd3fa80c7c200b7d6673f0f6
https://hal.inria.fr/hal-02188235

Zobrazit plný text záznamu

Interaction-Aware Tracking and Lane Change Detection in Highway Scenarios Using Realistic Driver Models

Autor: Sierra González, David, Romero-Cano, Víctor, Dibangoye, Jilles, Laugier, Christian

Publikováno v: ICRA 2017 Workshop on Robotics and Vehicular Technologies for Self-driving cars
ICRA 2017 Workshop on Robotics and Vehicular Technologies for Self-driving cars, Jun 2017, Singapore, Singapore

International audience; We address the problem of multi-vehicle tracking and motion prediction in highway scenarios using information from sensors and perception systems widely used in automated driving. In particular, we focus on the detection of la

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::fb47920feac1f9addabd4f3a2f7169c0
https://hal.inria.fr/hal-01534094

Zobrazit plný text záznamu

Interaction-Aware Driver Maneuver Inference in Highways Using Realistic Driver Models

Autor: David González, Christian Laugier, Jilles Steeve Dibangoye, Victor Romero-Cano

Publikováno v: Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC 2017)
Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC 2017), Oct 2017, Yokohama, Japan
ITSC

International audience; In this work, we address the problem of lane change maneuver prediction in highway scenarios using information from sensors and perception systems widely used in automated driving. Our prediction approach is twofold. First, a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9eddf1d2eac4cc9fc90ac45419c821a6
https://hal.inria.fr/hal-01589493

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání