14 citations · 44 across the 14 of their papers we have counts for
6 papers · 1 filter
Vehicle Trajectory Prediction on Highways Using Bird Eye View Representations and Deep Learning
Rubén Izquierdo, Álvaro Quintanar, David Fernández Llorca +4
This work presents a novel method for predicting vehicle trajectories in highway scenarios using efficient bird's eye view representations and convolutional neural networks. Vehicl…
Towards view-invariant vehicle speed detection from driving simulator images
Antonio Hernández Martínez, David Fernandez Llorca, Iván García Daza
The use of cameras for vehicle speed measurement is much more cost effective compared to other technologies such as inductive loops, radar or laser. However, accurate speed measure…
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…
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…
RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features
Javier Lorenzo, Ignacio Parra, Florian Wirth +3
Pedestrian crossing prediction is a crucial task for autonomous driving. Numerous studies show that an early estimation of the pedestrian's intention can decrease or even avoid a h…
Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs
R. Izquierdo, A. Quintanar, I. Parra +2
This paper describes a novel approach to perform vehicle trajectory predictions employing graphic representations. The vehicles are represented using Gaussian distributions into a…