Presents recent studies of deep learning and reinforcement learning for intelligent transportation
Focuses on popular topics including processing traffic data, transportation network representation, traffic flow forecasting, traffic signal control, automatic vehicle detection, traffic incident processing, travel demand prediction, and autonomous driving and driver behaviors
Provides new insights on how Big Data and Deep Learning can be used to build intelligent transportation systems to achieve safety and optimize performance and economy
Thanks for edits in advance
This book contributes to the progress towards intelligent transportation. It emphasizes new data management and machine learning approaches such as big data, deep learning and reinforcement learning. Deep learning and big data are very energetic and vital research topics of today´s technology. Road sensors, UAVs, GPS, CCTV and incident reports are sources of massive amount of data which are crucial to make serious traffic decisions. Herewith this substantial volume and velocity of data, it is challenging to build reliable prediction models based on machine learning methods and traditional relational database. Therefore, this book includes recent research works on big data, deep convolution networks and IoT-based smart solutions to limit the vehicle´s speed in a particular region, to support autonomous safe driving and to detect animals on roads for mitigating animal-vehicle accidents. This book serves broad readers including researchers, academicians, students and working professional in vehicles manufacturing, health and transportation departments and networking companies.
Part I: Big Data and Autonomous Vehicles.- Big Data Technologies with Computanational Model Computing using HADOOP with Scheduling Challeges.- Big Data for Autonomous Vehicles.- Part II: Deep Learning &Object detection for Safe driving.- Analysis of Target Detection and Tracking for Intelligent Vision System.- Enhanced end-to-end system for autonomous driving using deep convolutional networks.-Deep Learning Technologies to mitigate Deer-Vehicle Collisions.- Night-to-Day Road Scene Translation Using Generative Adversarial Network with Structural Similarity Loss for Night Driving Safety.- Safer-Driving: Application of Deep Transfer Learning to Build Intelligent Transportation Systems.- Leveraging CNN Deep Learning Model for Smart Parking.- Estimating Crowd Size for Public Place Surveillance using Deep Learning.- Part III: AI & IoT for intelligent transportation.- IoT Based Regional Speed Restriction Using Smart Sign Boards.- Synergy of Internet of Things with Cloud, Artificial Intelligence and Blockchain for Empowering Autonomous Vehicles.- Combining Artificial Intelligence with Robotic Process Automation - An Intelligent Automation Approach.
This book contributes to the progress towards intelligent transportation. It emphasizes new data management and machine learning approaches such as big data, deep learning and reinforcement learning. Deep learning and big data are very energetic and vital research topics of today's technology. Road sensors, UAVs, GPS, CCTV and incident reports are sources of massive amount of data which are crucial to make serious traffic decisions. Herewith this substantial volume and velocity of data, it is challenging to build reliable prediction models based on machine learning methods and traditional relational database. Therefore, this book includes recent research works on big data, deep convolution networks and IoT-based smart solutions to limit the vehicle's speed in a particular region, to support autonomous safe driving and to detect animals on roads for mitigating animal-vehicle accidents. This book serves broad readers including researchers, academicians, students and working professional in vehicles manufacturing, health and transportation departments and networking companies.
Inhaltsverzeichnis
Part I: Big Data and Autonomous Vehicles.- Big Data Technologies with Computanational Model Computing using HADOOP with Scheduling Challeges.- Big Data for Autonomous Vehicles.- Part II: Deep Learning &Object detection for Safe driving.- Analysis of Target Detection and Tracking for Intelligent Vision System.- Enhanced end-to-end system for autonomous driving using deep convolutional networks.-Deep Learning Technologies to mitigate Deer-Vehicle Collisions.- Night-to-Day Road Scene Translation Using Generative Adversarial Network with Structural Similarity Loss for Night Driving Safety.- Safer-Driving: Application of Deep Transfer Learning to Build Intelligent Transportation Systems.- Leveraging CNN Deep Learning Model for Smart Parking.- Estimating Crowd Size for Public Place Surveillance using Deep Learning.- Part III: AI & IoT for intelligent transportation.- IoT Based Regional Speed Restriction Using Smart Sign Boards.- Synergy of Internet of Things with Cloud, Artificial Intelligence and Blockchain for Empowering Autonomous Vehicles.- Combining Artificial Intelligence with Robotic Process Automation - An Intelligent Automation Approach.
Klappentext
This book contributes to the progress towards intelligent transportation. It emphasizes new data management and machine learning approaches such as big data, deep learning and reinforcement learning. Deep learning and big data are very energetic and vital research topics of today's technology. Road sensors, UAVs, GPS, CCTV and incident reports are sources of massive amount of data which are crucial to make serious traffic decisions. Herewith this substantial volume and velocity of data, it is challenging to build reliable prediction models based on machine learning methods and traditional relational database. Therefore, this book includes recent research works on big data, deep convolution networks and IoT-based smart solutions to limit the vehicle's speed in a particular region, to support autonomous safe driving and to detect animals on roads for mitigating animal-vehicle accidents. This book serves broad readers including researchers, academicians, students and working professional in vehicles manufacturing, health and transportation departments and networking companies.
Presents recent studies of deep learning and reinforcement learning for intelligent transportation
Focuses on popular topics including processing traffic data, transportation network representation, traffic flow forecasting, traffic signal control, automatic vehicle detection, traffic incident processing, travel demand prediction, and autonomous driving and driver behaviors
Provides new insights on how Big Data and Deep Learning can be used to build intelligent transportation systems to achieve safety and optimize performance and economy
Thanks for edits in advance
