Object detection using deep learning methods in traffic scenarios
A Boukerche, Z Hou - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
The recent boom of autonomous driving nowadays has made object detection in traffic
scenes a hot topic of research. Designed to classify and locate instances in the image, this is …
scenes a hot topic of research. Designed to classify and locate instances in the image, this is …
End to end learning for self-driving cars
We trained a convolutional neural network (CNN) to map raw pixels from a single front-
facing camera directly to steering commands. This end-to-end approach proved surprisingly …
facing camera directly to steering commands. This end-to-end approach proved surprisingly …
Deep learning for safe autonomous driving: Current challenges and future directions
Advances in information and signal processing technologies have a significant impact on
autonomous driving (AD), improving driving safety while minimizing the efforts of human …
autonomous driving (AD), improving driving safety while minimizing the efforts of human …
DLT-Net: Joint detection of drivable areas, lane lines, and traffic objects
Perception is an essential task for self-driving cars, but most perception tasks are usually
handled independently. We propose a unified neural network named DLT-Net to detect …
handled independently. We propose a unified neural network named DLT-Net to detect …
Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling
The detection of small road hazards, such as lost cargo, is a vital capability for self-driving
cars. We tackle this challenging and rarely addressed problem with a vision system that …
cars. We tackle this challenging and rarely addressed problem with a vision system that …
Beyond grand theft auto V for training, testing and enhancing deep learning in self driving cars
M Martinez, C Sitawarin, K Finch, L Meincke… - arXiv preprint arXiv …, 2017 - arxiv.org
As an initial assessment, over 480,000 labeled virtual images of normal highway driving
were readily generated in Grand Theft Auto V's virtual environment. Using these images, a …
were readily generated in Grand Theft Auto V's virtual environment. Using these images, a …
Survey of state-of-art autonomous driving technologies with deep learning
Y Huang, Y Chen - 2020 IEEE 20th international conference on …, 2020 - ieeexplore.ieee.org
This is a survey of autonomous driving technologies with deep learning methods. We
investigate the major fields of self-driving systems, such as perception, mapping and …
investigate the major fields of self-driving systems, such as perception, mapping and …
An empirical evaluation of deep learning on highway driving
B Huval, T Wang, S Tandon, J Kiske, W Song… - arXiv preprint arXiv …, 2015 - arxiv.org
Numerous groups have applied a variety of deep learning techniques to computer vision
problems in highway perception scenarios. In this paper, we presented a number of …
problems in highway perception scenarios. In this paper, we presented a number of …
Aerial images processing for car detection using convolutional neural networks: Comparison between faster r-cnn and yolov3
In this paper, we address the problem of car detection from aerial images using
Convolutional Neural Networks (CNN). This problem presents additional challenges as …
Convolutional Neural Networks (CNN). This problem presents additional challenges as …
Autonomous driving with deep learning: A survey of state-of-art technologies
Y Huang, Y Chen - arXiv preprint arXiv:2006.06091, 2020 - arxiv.org
Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007,
autonomous driving has been the most active field of AI applications. Almost at the same …
autonomous driving has been the most active field of AI applications. Almost at the same …