Vision-Based Autonomous Vehicle Control using the Two-Point Visual Driver Control Model

This work proposes a new self-driving framework that uses a human driver control model, whose feature-input values are extracted from images using deep convolutional neural networks (CNNs). The development of image processing techniques using CNNs along with accelerated computing hardware has recently enabled real-time detection of these feature-input values... (read more)

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Methods used in the Paper


METHOD TYPE
Average Pooling
Pooling Operations
Global Average Pooling
Pooling Operations
1x1 Convolution
Convolutions
Batch Normalization
Normalization
Max Pooling
Pooling Operations
Softmax
Output Functions
Convolution
Convolutions
Darknet-19
Convolutional Neural Networks
YOLOv2
Object Detection Models