Reprint

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

Edited by
September 2019
342 pages
  • ISBN978-3-03921-375-7 (Paperback)
  • ISBN978-3-03921-376-4 (PDF)

This book is a reprint of the Special Issue Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) that was published in

Computer Science & Mathematics
Engineering
Physical Sciences
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  and camera processing, ethics, and communications. Several new datasets are also provided for future research work. Researchers and others interested in these topics will find important advances contained in this book.

Format
  • Paperback
License
© 2019 by the authors; CC BY-NC-ND licence
Keywords
Vehicle-to-X communications; Intelligent Transport Systems; VANET; DSRC; Geobroadcast; multi-sensor; fusion; deep learning; LiDAR; camera; ADAS; object tracking; kernel based MIL algorithm; Gaussian kernel; adaptive classifier updating; perception in challenging conditions; obstacle detection and classification; dynamic path-planning algorithms; joystick; two-wheeled; terrestrial vehicle; path planning; infinity norm; p-norm; kinematic control; navigation; actuation systems; maneuver algorithm; automated driving; cooperative systems; communications; interface; automated-manual transition; driver monitoring; visual tracking; discriminative correlation filter bank; occlusion; sub-region; global region; autonomous vehicles; driving decision-making model; the emergency situations; red light-running behaviors; ethical and legal factors; T-S fuzzy neural network; road lane detection; map generation; driving assistance; autonomous driving; real-time object detection; autonomous driving assistance system; urban object detector; convolutional neural networks; machine vision; biological vision; deep learning; convolutional neural network; Gabor convolution kernel; recurrent neural network; enhanced learning; autonomous vehicle; crash injury severity prediction; support vector machine model; emergency decisions; relative speed; total vehicle mass of the front vehicle; perception in challenging conditions; obstacle detection and classification; dynamic path-planning algorithms; drowsiness detection; smart band; electrocardiogram (ECG); photoplethysmogram (PPG); recurrence plot (RP); convolutional neural network (CNN); squeeze-and-excitation; residual learning; depthwise separable convolution; blind spot detection; machine learning; neural networks; predictive; vehicle dynamics; electric vehicles; FPGA; GPU; parallel architectures; optimization; panoramic image dataset; road scene; object detection; deep learning; convolutional neural network; driverless; autopilot; deep leaning; object detection; generative adversarial nets; image inpainting; n/a