[HTML][HTML] Perception, planning, control, and coordination for autonomous vehicles
Autonomous vehicles are expected to play a key role in the future of urban transportation
systems, as they offer potential for additional safety, increased productivity, greater …
systems, as they offer potential for additional safety, increased productivity, greater …
Embedding structured contour and location prior in siamesed fully convolutional networks for road detection
Road detection from the perspective of moving vehicles is a challenging issue in
autonomous driving. Recently, many deep learning methods spring up for this task, because …
autonomous driving. Recently, many deep learning methods spring up for this task, because …
Efficient deep models for monocular road segmentation
This paper addresses the problem of road scene segmentation in conventional RGB images
by exploiting recent advances in semantic segmentation via convolutional neural networks …
by exploiting recent advances in semantic segmentation via convolutional neural networks …
Hybrid conditional random field based camera-LIDAR fusion for road detection
Road detection is one of the key challenges for autonomous vehicles. Two kinds of sensors
are commonly used for road detection: cameras and LIDARs. However, each of them suffers …
are commonly used for road detection: cameras and LIDARs. However, each of them suffers …
[PDF][PDF] Stixelnet: A deep convolutional network for obstacle detection and road segmentation.
General obstacle detection is a key enabler for obstacle avoidance in mobile robotics and
autonomous driving. In this paper we address the task of detecting the closest obstacle in …
autonomous driving. In this paper we address the task of detecting the closest obstacle in …
Learning collision-free space detection from stereo images: Homography matrix brings better data augmentation
Collision-free space detection is a critical component of autonomous vehicle perception. The
state-of-the-art algorithms are typically based on supervised deep learning. Their …
state-of-the-art algorithms are typically based on supervised deep learning. Their …
Exploiting fully convolutional neural networks for fast road detection
CCT Mendes, V Frémont… - 2016 IEEE International …, 2016 - ieeexplore.ieee.org
Road detection is a crucial task in autonomous navigation systems. It is responsible for
delimiting the road area and hence the free and valid space for maneuvers. In this paper, we …
delimiting the road area and hence the free and valid space for maneuvers. In this paper, we …
Deep deconvolutional networks for scene parsing
R Mohan - arXiv preprint arXiv:1411.4101, 2014 - arxiv.org
Scene parsing is an important and challenging prob-lem in computer vision. It requires
labeling each pixel in an image with the category it belongs to. Tradition-ally, it has been …
labeling each pixel in an image with the category it belongs to. Tradition-ally, it has been …
Spin road mapper: Extracting roads from aerial images via spatial and interaction space graph reasoning for autonomous driving
WGC Bandara, JMJ Valanarasu… - … Conference on Robotics …, 2022 - ieeexplore.ieee.org
Road extraction is an essential step in building autonomous navigation systems. Detecting
road segments is challenging as they are of varying widths, bifurcated throughout the image …
road segments is challenging as they are of varying widths, bifurcated throughout the image …
CRF based road detection with multi-sensor fusion
In this paper, we propose to fuse the LIDAR and monocular image in the framework of
conditional random field to detect the road robustly in challenging scenarios. LIDAR points …
conditional random field to detect the road robustly in challenging scenarios. LIDAR points …