Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)

Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS)

This book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR

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Bibliographic Details
Main Author:	Tang, Bo (auth)
Other Authors:	Ball, John (auth)
Format:	Book Chapter
Published:	MDPI - Multidisciplinary Digital Publishing Institute 2019
Subjects:	n/a FPGA recurrence plot (RP) residual learning neural networks driver monitoring navigation depthwise separable convolution optimization dynamic path-planning algorithms object tracking sub-region cooperative systems convolutional neural networks DSRC VANET joystick road scene convolutional neural network (CNN) multi-sensor p-norm occlusion crash injury severity prediction deep leaning squeeze-and-excitation electric vehicles perception in challenging conditions T-S fuzzy neural network total vehicle mass of the front vehicle electrocardiogram (ECG) communications generative adversarial nets camera adaptive classifier updating Vehicle-to-X communications convolutional neural network predictive Geobroadcast infinity norm urban object detector machine learning automated-manual transition red light-running behaviors photoplethysmogram (PPG) panoramic image dataset parallel architectures visual tracking autopilot ADAS kinematic control GPU road lane detection obstacle detection and classification Gabor convolution kernel autonomous vehicle Intelligent Transport Systems driving decision-making model Gaussian kernel autonomous vehicles enhanced learning ethical and legal factors kernel based MIL algorithm image inpainting fusion terrestrial vehicle driverless drowsiness detection map generation object detection interface machine vision driving assistance blind spot detection deep learning relative speed autonomous driving assistance system discriminative correlation filter bank recurrent neural network emergency decisions LiDAR real-time object detection vehicle dynamics path planning actuation systems maneuver algorithm autonomous driving smart band the emergency situations two-wheeled support vector machine model global region biological vision automated driving
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Description
Summary:	This book contains the latest research on machine learning and embedded computing in advanced driver assistance systems (ADAS). It encompasses research in detection, tracking, LiDAR
Physical Description:	1 electronic resource (344 p.)
ISBN:	books978-3-03921-376-4 9783039213757 9783039213764
Access:	Open Access