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Palabras clave – provistas por la editorial

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