12 citations · 18 across the 5 of their papers we have counts for
8 papers
Towards Explainable Motion Prediction using Heterogeneous Graph Representations
Sandra Carrasco Limeros, Sylwia Majchrowska, Joakim Johnander +2
Motion prediction systems aim to capture the future behavior of traffic scenarios enabling autonomous vehicles to perform safe and efficient planning. The evolution of these scenar…
Data-driven vehicle speed detection from synthetic driving simulator images
Antonio Hernández Martínez, Javier Lorenzo Díaz, Iván García Daza +1
Despite all the challenges and limitations, vision-based vehicle speed detection is gaining research interest due to its great potential benefits such as cost reduction, and enhanc…
From driving automation systems to autonomous vehicles: clarifying the terminology
David Fernández Llorca
The terminological landscape is rather cluttered when referring to autonomous driving or vehicles. A plethora of terms are used interchangeably, leading to misuse and confusion. Wi…
Fail-Aware LIDAR-Based Odometry for Autonomous Vehicles
Iván García Daza, Monica Rentero, Carlota Salinas Maldonado +4
Autonomous driving systems are set to become a reality in transport systems and, so, maximum acceptance is being sought among users. Currently, the most advanced architectures requ…
SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs
Sandra Carrasco, David Fernández Llorca, Miguel Ángel Sotelo
Autonomous vehicles navigate in dynamically changing environments under a wide variety of conditions, being continuously influenced by surrounding objects. Modelling interactions a…
3D-DEEP: 3-Dimensional Deep-learning based on elevation patterns forroad scene interpretation
A. Hernández, S. Woo, H. Corrales +4
Road detection and segmentation is a crucial task in computer vision for safe autonomous driving. With this in mind, a new net architecture (3D-DEEP) and its end-to-end training me…