55 citations · 88 across the 11 of their papers we have counts for
5 papers · 1 filter
The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?
A. Quintanar, R. Izquierdo, I. Parra +2
While driving on highways, every driver tries to be aware of the behavior of surrounding vehicles, including possible emergency braking, evasive maneuvers trying to avoid obstacles…
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…
Vehicle Ego-Lane Estimation with Sensor Failure Modeling
Augusto Luis Ballardini, Daniele Cattaneo, Rubén Izquierdo +4
We present a probabilistic ego-lane estimation algorithm for highway-like scenarios that is designed to increase the accuracy of the ego-lane estimate, which can be obtained relyin…