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

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

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