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Accelerating the Response of Self-Driving Control by Using Rapid Object Detection and Steering Angle Prediction

https://doi.org/10.3390/electronics12102161

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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CBAM: Convolutional Block Attention Module

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Neural controller of autonomous driving mobile robot by an embedded camera

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Training a Remote-Control Car to Autonomously Lane-Follow using End-to-End Neural Networks

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Convolutional neural networks at constrained time cost

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Deep Residual Learning for Image Recognition

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Scaled-YOLOv4: Scaling Cross Stage Partial Network

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CSPNet: A New Backbone that can Enhance Learning Capability of CNN

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Navigating Self-Driving Vehicles Using Convolutional Neural Network

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Real-Time Self-Driving Car Navigation Using Deep Neural Network

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Development Of Autonomous Downscaled Model Car Using Neural Networks And Machine Learning

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CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features

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Working model of Self-driving car using Convolutional Neural Network, Raspberry Pi and Arduino

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Sound Source Localization Method Based on Densely Connected Convolutional Neural Network

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Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation

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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

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Backpropagation Applied to Handwritten Zip Code Recognition

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1/10th scale autonomous vehicle based on convolutional neural network

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DropBlock: A regularization method for convolutional networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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MICH-Net: A Novel Deep Learning Architecture With African Fire Hawk Optimization for Steering Angle Prediction in an Advanced Driver Assistance System

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Feature-Optimized Deep Attention Learning for Steering Angle Prediction in Autonomous Ground Vehicles

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