… 6 THE APPLICATION IN AUTONOMOUS DRIVING In autonomous driving, deep convolution
networks can be utilized in many aspects, such as vehicle and pedestrian detection, vision …

… we review relevant work from the perspective of autonomous driving. First, we cover
engineered approaches applied in practice in the self-driving industry. Then, we discuss machine …

… for image classification and object detection and Autonomous Driving Systems (ADSs) in
a … in convolutional operations. Results of a novel investigation of the convolution types and …

… suitable for real-time decisionmaking of autonomous vehicles. Therefore, to overcome
the … efficient and fast network called graph-based spatial-temporal convolutional network (GSTCN)…

… neural networks for autonomous driving. In this paper, we address the above issues by
presenting SqueezeDet, a fully convolutional neural network … is also a fully-convolutional network. …

… propose using a Convolutional Neural Network (CNN) to learn safe driving behavior and
smooth steering maneuvering as an empowerment of autonomous driving technologies. The …

… network models that generate driving control signals directly from input images. In contrast
to prior work that segments the autonomous driving … network to allow the convolutional net to …

… autonomous driving of autonomous vehicles. This application is implemented with …
Convolutional Network (FCN) architectures obtained by modifying the Convolutional Neural Network …

Autonomous driving demands accurate perception and safe decision-making. To achieve this,
automated vehicles are typically equipped with multiple sensors (eg, cameras, Lidar, etc.), …

… in order to support autonomous driving of autonomous vehicles using deep … Convolutional
Network (FCN) architectures obtained by making changes in Convolutional Neural Network (…