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Author: Neha Sharma
Publisher:
ISBN:
Category : Intelligent transportation systems
Languages : en
Pages : 230
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Book Description
Traffic congestion is one of the most prevalent transport problems in large cities like Auckland. Building new roadways is often considered the most effective way to mitigate traffic congestion. However the most efficient and cost effective way to combat congestion is the use of Intelligent Transportation System (ITS) applications. Intelligent Transportation Systems (ITS) are advanced applications which integrate information and technology with the available transport infrastructure to provide a better, safe and efficient transportation network [10]. Sydney Coordinated Adaptive Traffic Systems (SCATS) is an ITS application deployed across New Zealand. It detects real time traffic data to dynamically change traffic signal timing to make best use of the road infrastructure. SCATS Ramp Metering System (SRMS) is another traffic management tool that controls motorway traffic during congestion. These ITS applications require real time data from their employed vehicle detector to function. Inductive loop detectors (ILD) are employed by SCATS to gather traffic data. There are more than 8000 inductive loops placed on SCATS controlled intersection in Auckland and over 4000 dual inductive loops on Auckland motorway. This thesis proposes a speed detection algorithm that uses these already deployed SCATS inductive loop detectors to measure vehicle speed. A vehicle classification algorithm is also presented that can distinguish between three vehicle classes. Speed estimation plays a crucial role in traffic management as it is an important indicator of traffic condition. The speed estimation algorithm presented can predict vehicle speed from a SCATS inductive loop detector with an accuracy of ±5.89 km/hr. Unlike other speed algorithms currently being used across New Zealand (NZ), the proposed speed model does not work on any assumptions and remains accurate for all traffic conditions. Widely deployed dual inductive loops across NZ are currently used to classify vehicles into four categories by measuring vehicle length. The proposed classification algorithm works on one inductive loop detector to produce a recognition rate of 100%. The algorithm can accurately predict vehicle class of a passenger car, a van and a sports-utility vehicle (SUV).
Author: Neha Sharma
Publisher:
ISBN:
Category : Intelligent transportation systems
Languages : en
Pages : 230
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Book Description
Traffic congestion is one of the most prevalent transport problems in large cities like Auckland. Building new roadways is often considered the most effective way to mitigate traffic congestion. However the most efficient and cost effective way to combat congestion is the use of Intelligent Transportation System (ITS) applications. Intelligent Transportation Systems (ITS) are advanced applications which integrate information and technology with the available transport infrastructure to provide a better, safe and efficient transportation network [10]. Sydney Coordinated Adaptive Traffic Systems (SCATS) is an ITS application deployed across New Zealand. It detects real time traffic data to dynamically change traffic signal timing to make best use of the road infrastructure. SCATS Ramp Metering System (SRMS) is another traffic management tool that controls motorway traffic during congestion. These ITS applications require real time data from their employed vehicle detector to function. Inductive loop detectors (ILD) are employed by SCATS to gather traffic data. There are more than 8000 inductive loops placed on SCATS controlled intersection in Auckland and over 4000 dual inductive loops on Auckland motorway. This thesis proposes a speed detection algorithm that uses these already deployed SCATS inductive loop detectors to measure vehicle speed. A vehicle classification algorithm is also presented that can distinguish between three vehicle classes. Speed estimation plays a crucial role in traffic management as it is an important indicator of traffic condition. The speed estimation algorithm presented can predict vehicle speed from a SCATS inductive loop detector with an accuracy of ±5.89 km/hr. Unlike other speed algorithms currently being used across New Zealand (NZ), the proposed speed model does not work on any assumptions and remains accurate for all traffic conditions. Widely deployed dual inductive loops across NZ are currently used to classify vehicles into four categories by measuring vehicle length. The proposed classification algorithm works on one inductive loop detector to produce a recognition rate of 100%. The algorithm can accurately predict vehicle class of a passenger car, a van and a sports-utility vehicle (SUV).
Author: Alberto Broggi
Publisher: World Scientific
ISBN: 9789810237202
Category : Computers
Languages : en
Pages : 260
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Book Description
This book surveys the history of automatic vehicle guidance based on the processing of visual information, starting from the very first projects worldwide up to the latest developments. It also presents the ARGO prototype vehicle, developed at the University of Parma (Italy), and describes its equipment, setup, and performance. ARGO has been equipped with cameras and processing systems to drive autonomously in real traffic conditions. The complete system has been tested on public roads, during a tour in which ARGO drove itself along the Italian highway network for more than 2000 km. A detailed analysis of this trip is also included.
Author: Jiadi Yu
Publisher: Springer
ISBN: 3319897705
Category : Computers
Languages : en
Pages : 81
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Book Description
This SpringerBrief begins by introducing the concept of smartphone sensing and summarizing the main tasks of applying smartphone sensing in vehicles. Chapter 2 describes the vehicle dynamics sensing model that exploits the raw data of motion sensors (i.e., accelerometer and gyroscope) to give the dynamic of vehicles, including stopping, turning, changing lanes, driving on uneven road, etc. Chapter 3 detects the abnormal driving behaviors based on sensing vehicle dynamics. Specifically, this brief proposes a machine learning-based fine-grained abnormal driving behavior detection and identification system, D3, to perform real-time high-accurate abnormal driving behaviors monitoring using the built-in motion sensors in smartphones. As more vehicles taking part in the transportation system in recent years, driving or taking vehicles have become an inseparable part of our daily life. However, increasing vehicles on the roads bring more traffic issues including crashes and congestions, which make it necessary to sense vehicle dynamics and detect driving behaviors for drivers. For example, sensing lane information of vehicles in real time can be assisted with the navigators to avoid unnecessary detours, and acquiring instant vehicle speed is desirable to many important vehicular applications. Moreover, if the driving behaviors of drivers, like inattentive and drunk driver, can be detected and warned in time, a large part of traffic accidents can be prevented. However, for sensing vehicle dynamics and detecting driving behaviors, traditional approaches are grounded on the built-in infrastructure in vehicles such as infrared sensors and radars, or additional hardware like EEG devices and alcohol sensors, which involves high cost. The authors illustrate that smartphone sensing technology, which involves sensors embedded in smartphones (including the accelerometer, gyroscope, speaker, microphone, etc.), can be applied in sensing vehicle dynamics and driving behaviors. Chapter 4 exploits the feasibility to recognize abnormal driving events of drivers at early stage. Specifically, the authors develop an Early Recognition system, ER, which recognize inattentive driving events at an early stage and alert drivers timely leveraging built-in audio devices on smartphones. An overview of the state-of-the-art research is presented in chapter 5. Finally, the conclusions and future directions are provided in Chapter 6.
Author: Yufang Zhang
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 140
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Book Description
Author: Huseyin Abut
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110981319
Category : Technology & Engineering
Languages : en
Pages : 490
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Book Description
This book features works from world-class experts from academia, industry, and national agencies focusing on a wide spectrum of automotive fields towards humanvehicle harmonization covering in-vehicle signal processing, driver modeling, systems and safety. The essays collected in this volume present cutting-edge studies on safety, driver behavior, infrastructure, and human-to-vehicle interfaces.
Author: Luz Elena Yañez Mimbela
Publisher:
ISBN:
Category : Detectors
Languages : en
Pages : 0
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Book Description
Author: Lawrence A. Klein
Publisher: SPIE-International Society for Optical Engineering
ISBN: 9781510627642
Category : Algorithms
Languages : en
Pages : 235
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Book Description
"Sensor and Data Fusion for Intelligent Transportation Systems introduces readers to the roles of the data fusion processes defined by the Joint Directors of Laboratories (JDL) data fusion model, data fusion algorithms, and noteworthy applications of data fusion to ITS. Additionally, the monograph offers detailed descriptions of three of the widely applied data fusion techniques and their relevance to ITS (namely, Bayesian inference, Dempster-Shafer evidential reasoning, and Kalman filtering), and indicates directions for future research in the area of data fusion. The focus is on data fusion algorithms rather than on sensor and data fusion architectures, although the book does summarize factors that influence the selection of a fusion architecture and several architecture frameworks"--
Author:
Publisher:
ISBN:
Category : Automobiles
Languages : en
Pages : 116
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Book Description
Author: Hong Cheng
Publisher: Springer Science & Business Media
ISBN: 1447122801
Category : Computers
Languages : en
Pages : 151
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Book Description
This important text/reference presents state-of-the-art research on intelligent vehicles, covering not only topics of object/obstacle detection and recognition, but also aspects of vehicle motion control. With an emphasis on both high-level concepts, and practical detail, the text links theory, algorithms, and issues of hardware and software implementation in intelligent vehicle research. Topics and features: presents a thorough introduction to the development and latest progress in intelligent vehicle research, and proposes a basic framework; provides detection and tracking algorithms for structured and unstructured roads, as well as on-road vehicle detection and tracking algorithms using boosted Gabor features; discusses an approach for multiple sensor-based multiple-object tracking, in addition to an integrated DGPS/IMU positioning approach; examines a vehicle navigation approach using global views; introduces algorithms for lateral and longitudinal vehicle motion control.
Author: John Hansen
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110669781
Category : Computers
Languages : en
Pages : 327
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Book Description
This book presents works from world-class experts from academia, industry, and national agencies representing countries from across the world focused on automotive fields for in-vehicle signal processing and safety. These include cutting-edge studies on safety, driver behavior, infrastructure, and human-to-vehicle interfaces. Vehicle Systems, Driver Modeling and Safety is appropriate for researchers, engineers, and professionals working in signal processing for vehicle systems, next generation system design from driver-assisted through fully autonomous vehicles.
