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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| Year of Publication: | 2019 |
| Language: | English |
| Physical Description: | 1 electronic resource (344 p.) |
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| 041 | 0 | |a eng | |
| 100 | 1 | |a Tang, Bo |4 auth | |
| 245 | 1 | 0 | |a Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS) |
| 246 | |a Machine Learning and Embedded Computing in Advanced Driver Assistance Systems | ||
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2019 | ||
| 300 | |a 1 electronic resource (344 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 520 | |a 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 | ||
| 546 | |a English | ||
| 653 | |a FPGA | ||
| 653 | |a recurrence plot (RP) | ||
| 653 | |a residual learning | ||
| 653 | |a neural networks | ||
| 653 | |a driver monitoring | ||
| 653 | |a navigation | ||
| 653 | |a depthwise separable convolution | ||
| 653 | |a optimization | ||
| 653 | |a dynamic path-planning algorithms | ||
| 653 | |a object tracking | ||
| 653 | |a sub-region | ||
| 653 | |a cooperative systems | ||
| 653 | |a convolutional neural networks | ||
| 653 | |a DSRC | ||
| 653 | |a VANET | ||
| 653 | |a joystick | ||
| 653 | |a road scene | ||
| 653 | |a convolutional neural network (CNN) | ||
| 653 | |a multi-sensor | ||
| 653 | |a p-norm | ||
| 653 | |a occlusion | ||
| 653 | |a crash injury severity prediction | ||
| 653 | |a deep leaning | ||
| 653 | |a squeeze-and-excitation | ||
| 653 | |a electric vehicles | ||
| 653 | |a perception in challenging conditions | ||
| 653 | |a T-S fuzzy neural network | ||
| 653 | |a total vehicle mass of the front vehicle | ||
| 653 | |a electrocardiogram (ECG) | ||
| 653 | |a communications | ||
| 653 | |a generative adversarial nets | ||
| 653 | |a camera | ||
| 653 | |a adaptive classifier updating | ||
| 653 | |a Vehicle-to-X communications | ||
| 653 | |a convolutional neural network | ||
| 653 | |a predictive | ||
| 653 | |a Geobroadcast | ||
| 653 | |a infinity norm | ||
| 653 | |a urban object detector | ||
| 653 | |a machine learning | ||
| 653 | |a automated-manual transition | ||
| 653 | |a red light-running behaviors | ||
| 653 | |a photoplethysmogram (PPG) | ||
| 653 | |a panoramic image dataset | ||
| 653 | |a parallel architectures | ||
| 653 | |a visual tracking | ||
| 653 | |a autopilot | ||
| 653 | |a ADAS | ||
| 653 | |a kinematic control | ||
| 653 | |a GPU | ||
| 653 | |a road lane detection | ||
| 653 | |a obstacle detection and classification | ||
| 653 | |a Gabor convolution kernel | ||
| 653 | |a autonomous vehicle | ||
| 653 | |a Intelligent Transport Systems | ||
| 653 | |a driving decision-making model | ||
| 653 | |a Gaussian kernel | ||
| 653 | |a autonomous vehicles | ||
| 653 | |a enhanced learning | ||
| 653 | |a ethical and legal factors | ||
| 653 | |a kernel based MIL algorithm | ||
| 653 | |a image inpainting | ||
| 653 | |a fusion | ||
| 653 | |a terrestrial vehicle | ||
| 653 | |a driverless | ||
| 653 | |a drowsiness detection | ||
| 653 | |a map generation | ||
| 653 | |a object detection | ||
| 653 | |a interface | ||
| 653 | |a machine vision | ||
| 653 | |a driving assistance | ||
| 653 | |a blind spot detection | ||
| 653 | |a deep learning | ||
| 653 | |a relative speed | ||
| 653 | |a autonomous driving assistance system | ||
| 653 | |a discriminative correlation filter bank | ||
| 653 | |a recurrent neural network | ||
| 653 | |a emergency decisions | ||
| 653 | |a LiDAR | ||
| 653 | |a real-time object detection | ||
| 653 | |a vehicle dynamics | ||
| 653 | |a path planning | ||
| 653 | |a actuation systems | ||
| 653 | |a maneuver algorithm | ||
| 653 | |a autonomous driving | ||
| 653 | |a smart band | ||
| 653 | |a the emergency situations | ||
| 653 | |a two-wheeled | ||
| 653 | |a support vector machine model | ||
| 653 | |a global region | ||
| 653 | |a biological vision | ||
| 653 | |a automated driving | ||
| 776 | |z 3-03921-375-X | ||
| 700 | 1 | |a Ball, John |4 auth | |
| 906 | |a BOOK | ||
| ADM | |b 2024-02-27 23:13:06 Europe/Vienna |f system |c marc21 |a 2020-02-01 22:26:53 Europe/Vienna |g false | ||
| AVE | |i DOAB Directory of Open Access Books |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338789360004498&Force_direct=true |Z 5338789360004498 |b Available |8 5338789360004498 | ||