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This document discusses research on smart cars and intelligent transportation systems. It describes how current vehicles use sensors and computer systems to detect surroundings and operate safely. The researchers expect that within several years, advanced automation and smart driving assistance will become standard in vehicles. The document also reviews literature on implementing safety and technology systems in smart vehicles and infrastructure to communicate and make decisions to increase safety and efficiency.
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This document describes a project to develop a lane and speed breaker detection system for autonomous vehicles using machine learning algorithms. The system aims to enhance safety and efficiency by detecting lane markings and speed breakers. It will use sensors and machine learning models to process real-time data and identify lanes and speed breakers to warn the vehicle's control system. The project scope involves developing algorithms and models, collecting a diverse road dataset, implementing real-time detection, and testing the system.
Icquest1518
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IRJET- Lane Detection using Neural Networks
This document discusses a proposed method for lane detection using neural networks. Specifically, it proposes using a fully convolutional neural network for lane feature extraction from images. The network is trained using a detection loss function that considers both lane classification and regression. Experimental results show the classification network achieves over 97.5% accuracy and the full detection model achieves 82.24% accuracy on 29 road scenes. Existing lane detection methods are also discussed, including those using color/shape models, Hough transforms, and convolutional neural networks.
IRJET- Study of Automated Highway System
1. The document discusses an automated highway system (AHS) which aims to increase both the safety and efficiency of highways by automating vehicles. Sensors and microprocessors would allow vehicles to sense their environment and react without driver input, reducing accidents caused by human error.
2. An AHS would have four key components - lateral motion control to keep vehicles in lanes, longitudinal motion control to maintain distance and speed between vehicles, and obstacle detection and avoidance capabilities. Sensors like cameras, radar and sonar would be used to detect lanes, other vehicles, and obstacles.
3. An AHS could significantly increase road capacity by allowing vehicles to travel closer together at higher speeds. It may also reduce fuel consumption,
Recommended
Smart cars (1)
This document discusses research on smart cars and intelligent transportation systems. It describes how current vehicles use sensors and computer systems to detect surroundings and operate safely. The researchers expect that within several years, advanced automation and smart driving assistance will become standard in vehicles. The document also reviews literature on implementing safety and technology systems in smart vehicles and infrastructure to communicate and make decisions to increase safety and efficiency.
IRJET - Smart Traffic Monitoring System
The document describes a proposed smart traffic monitoring system that uses image processing and a Raspberry Pi microcontroller to automatically adjust traffic light timing based on detected traffic density. Video is captured of intersections and processed to detect vehicles and determine traffic density on each road. The number of vehicles is then used to calculate the optimal traffic light timing, with longer green lights allocated to heavier traffic. This provides an adaptive system that is more efficient than fixed-time traffic lights that cannot adjust to changing traffic conditions.
john dos
Collision avoidance research has focused on vehicle-to-vehicle (v2v), vehicle-to-road (v2r), and road-to-road (r2r) communication. V2v technologies use radar, cameras, or radio to prevent collisions, while v2r systems provide intersection warnings. R2r systems independently sense vehicle information in real-time. Several US universities are conducting intersection collision avoidance research projects using sensors and wireless technologies, though relying solely on vehicle equipment has drawbacks. Alternative approaches use road sensors transmitting traffic data to a base station for predictive collision analysis and warnings. However, current routing implementations result in unacceptable message latency for collision avoidance. A commercial product uses sensors and wireless access points but suffers
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5G connectivity in automobiles has the potential to transform the driving experience and enable innovative technologies. It provides faster internet connectivity, supporting advanced infotainment systems and data-intensive applications like AR navigation. 5G enhances safety features like collision avoidance systems and emergency response through real-time vehicle communication. It improves traffic management with cooperative adaptive cruise control and collision avoidance between connected vehicles. Challenges to implementing 5G in automobiles include interference, infrastructure needs, security concerns, and regulatory compliance.
Detection of Lane and Speed Breaker.pptx
This document describes a project to develop a lane and speed breaker detection system for autonomous vehicles using machine learning algorithms. The system aims to enhance safety and efficiency by detecting lane markings and speed breakers. It will use sensors and machine learning models to process real-time data and identify lanes and speed breakers to warn the vehicle's control system. The project scope involves developing algorithms and models, collecting a diverse road dataset, implementing real-time detection, and testing the system.
Icquest1518
This document summarizes a proposed system called Density Based Signal Management in Traffic System that aims to optimize traffic light timing based on real-time traffic density readings. Road Side Units would monitor vehicle density on all sides of an intersection and prioritize which side receives the green light based on current traffic conditions, with the goal of clearing traffic more efficiently. This approach could help reduce traffic jams and delays by dynamically adjusting light timing based on measured vehicle accumulation rather than fixed schedules.
IRJET- Lane Detection using Neural Networks
This document discusses a proposed method for lane detection using neural networks. Specifically, it proposes using a fully convolutional neural network for lane feature extraction from images. The network is trained using a detection loss function that considers both lane classification and regression. Experimental results show the classification network achieves over 97.5% accuracy and the full detection model achieves 82.24% accuracy on 29 road scenes. Existing lane detection methods are also discussed, including those using color/shape models, Hough transforms, and convolutional neural networks.
IRJET- Study of Automated Highway System
1. The document discusses an automated highway system (AHS) which aims to increase both the safety and efficiency of highways by automating vehicles. Sensors and microprocessors would allow vehicles to sense their environment and react without driver input, reducing accidents caused by human error.
2. An AHS would have four key components - lateral motion control to keep vehicles in lanes, longitudinal motion control to maintain distance and speed between vehicles, and obstacle detection and avoidance capabilities. Sensors like cameras, radar and sonar would be used to detect lanes, other vehicles, and obstacles.
3. An AHS could significantly increase road capacity by allowing vehicles to travel closer together at higher speeds. It may also reduce fuel consumption,
journal publications
Our journal has been unwavering commitment to showcasing cutting-edge research. The journal provides a platform for researchers to disseminate their work on next-generation technologies. In an era where innovation is the driving force behind progress, JST plays a crucial role in shaping the discourse on emerging technologies, thus contributing to their rapid development and implementation.
IRJET- Image Processing based Intelligent Traffic Control and Monitoring ...
This document summarizes a research paper on an intelligent traffic control and monitoring system using image processing and the Internet of Things. The system aims to reduce traffic congestion by controlling traffic lights based on real-time traffic density detected through image processing of vehicle images. It consists of hardware and software modules. The hardware uses cameras to capture vehicle images and the software uses image processing techniques like object detection and classification to detect and count vehicles in real-time and estimate traffic density. This information is then used to dynamically adjust traffic light timings with the goal of optimizing traffic flow and reducing waiting times at signals. The system is meant to provide a more efficient solution to traffic management than conventional fixed-time traffic light control systems.
Semiconductors in Automotive Industry The Rise of Dynamic PAT and Advanced Ou...
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The automotive industry is undergoing significant transformations in the realm of semiconductor technologies utilized in vehicles. With the increasing number of chips in cars and the growing levels of automation, traditional part average testing (PAT) methods are no longer sufficient to ensure the desired levels of quality and reliability.
While PAT has been a prevalent practice in the automotive sector for nearly three decades, relying on statistical control limits to enhance yield and end-of-the-line quality, the emergence of advanced AI systems and autonomous driving technologies necessitates the adoption of more sophisticated outlier detection techniques and enhanced inspection and test coverage.
The VII propject-V3
This document provides an overview of a graduation project on vehicle infrastructure integration (VII) conducted by a team of students at Ain Shams University in Egypt. The project aims to reduce traffic accidents and monitor traffic by enabling communication between vehicles and between vehicles and roadside infrastructure using wireless technology. It discusses technical issues around communication methods, GPS tracking, microcontrollers and interfaces. It also outlines the system components, hardware, applications including intersection collision avoidance and monitoring systems, and the future work planned for DSRC and wireless communications kits.
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ACCIDENT DETECTION AND AVOIDANCE USING VEHICLE TO VEHICLE COMMUNICATION (V2V)
The document describes a proposed vehicle accident detection and avoidance system using vehicle-to-vehicle (V2V) communication. The system would use sensors like accelerometers, crash sensors, vibration sensors, alcohol sensors, GPS, and GSM modules to detect accidents and drunk driving in real-time. When an accident or drunk driving incident is detected, the system would send alerts with the vehicle's location to emergency responders. It would also use V2V communication to warn other nearby vehicles of the situation via the NRF24L01 wireless module. The system aims to reduce accidents and save lives by quickly notifying authorities and preventing further collisions. It additionally includes an automated parking feature to safely park a vehicle if drunk driving is detected
Study of Estimation of Road Roughness Condition and Ghat Complexity Analysis ...
This document presents a study on estimating road roughness condition and ghat (mountain pass) complexity using sensors in smartphones. The proposed system utilizes GPS and sensors like accelerometers and magnetometers in Android phones to analyze road bumps and the number of turns in ghats. Data collected from phone sensors is sent to a server for analysis. The server evaluates road conditions and makes information available to users via an app. This allows users to view road conditions and choose alternative routes if needed. Algorithms for detecting road bumps and calculating distance between locations are described. The system aims to provide low-cost continuous monitoring of road infrastructure using smartphones.
Real Time Road Blocker Detection and Distance Calculation for Autonomous Vehi...
Real Time Road Blocker Detection and Distance Calculation for Autonomous Vehi...Associate Professor in VSB Coimbatore
Statistics indicate that most road accidents occur due to a lack of time to react to instant traffic. This problem can be addressed with self-driving vehicles with the application of automated systems to detect such traffic events. The Autonomous Vehicle Navigation System (ATS) has been a standard in the Intelligent Transport System (ITS) and many Driver Assistance Systems (DAS) have been adopted to support these Advanced Autonomous Vehicles (IAVs). To develop these recognition systems for automated self-driving cars, it's important to monitor and operate in real-time traffic events. It requires the correct detection and response of traffic event an automated vehicle. In this paper proposed to develop such a system by applying image recognition to detect and respond to a road blocker by means of real-time distance measurement. To study the performance by measuring accuracy and precision of road blocker detection system and distance calculation, various experiments were conducted by using Shalom frame dataset and detection accuracy, precision of 99%, 100%, while distance calculation 97%, 99% has been achieved by this approach.smart traffic control system using canny edge detection algorithm (4).pdf
The document is a project report submitted by G.Yamini for the partial fulfillment of an MCA degree. It includes a declaration signed by G.Yamini stating that the project titled "SMART TRAFFIC CONTROL SYSTEM USING CANNY EDGE DETECTION ALGORITHM" is her original work and has not been submitted elsewhere. It also includes certificates from the guide and institution confirming that the project is G.Yamini's original work. The project report describes implementing a smart traffic control system using image processing and Canny edge detection algorithm.
Ijetr042176
This document summarizes a research paper that proposes a system to help avoid vehicle collisions and guide parking using vehicular communication technologies. The system uses ultrasonic sensors to detect obstacles and allow vehicles to warn each other of hard braking or crashes to avoid collisions. It also enables emergency vehicles to broadcast their presence so other vehicles can make way. Additionally, infrared sensors help guide vehicles to available parking spaces to reduce traffic congestion from searching for parking. The system was developed as a prototype to integrate these three features and facilitate safer and more efficient driving and parking.
Traffic Signboard Classification with Voice alert to the driver.pptx
The basic idea of proposed system is to provide alertness to the driver about the presence of traffic signboard at a particular distance apart. It generates a warning to the driver in advance of any danger. The warning allows the driver to take appropriate actions in order to avoid the accident.The system takes continuous video input from the console monitor or camera installed on the car's bonnet. The underlying algorithm extracts the features of the input image and matches them with an existing library of traffic sign.
The output is fed to the driving assistance system and in turn drives the car accordingly. We developed this intelligent system using Machine Learning.This device will take camera feeds and upgrade the system
instantaneously.
Density Based Traffic System with Emergency override using Bluetooth
This document is a project report submitted by four students for their bachelor's degree. It outlines a density based traffic system with emergency override using RFID. The proposed system allocates different time slots to roads based on vehicle density to improve time management. It also provides priority traffic signal management for high density lanes and emergency vehicles. An additional feature allows pedestrians to safely cross the road based on their request. The project implements a prototype for an automatic and safer traffic signal management system based on emergency conditions.
VANET BASED SECURED ACCIDENT PREVENTION SYSTEM
This document proposes a notification service to prevent accidents using vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. The system would warn drivers about accidents and hazardous road conditions using messages of different priorities sent between vehicles and roadside units (RSUs). A VANET simulation shows how safety messages reduce driver response time during emergencies. The new communication system improves bandwidth usage for low priority messages containing traffic and weather data shared between RSUs. The simulation results demonstrate that intelligent transportation systems can significantly decrease driver response times and improve road safety.
INTELLIGENT TRANSPORT SYSTEM (ITS)
Why it’s Needed?
Traffic congestion-insufficient road development-growing number of vehicles.
Low speed, increased accident rates, increased fuel consumption, and increased pollution.
Impossible to build enough new roads or to meet the demand.
These explore the concepts that treat highway systems and the vehicles that use them as integrated system. Among them is the concept of Intelligent Transportation Systems.
The goal of I T S is to improve the transportation system to make it more efficient and safer by use of information, communications and control technologies.
India is going through a period of drastic change in the transportation area due to:
Rapidly growing economy.
Insufficient and inadequate public transportation system.
Rising vehicle ownership levels.
ITS PARTS
I T S ARCHITECTURE
· Framework for planning, defining, and integrating intelligent transportation systems.
Benefits of Architecture
Reduces time and resources required to integrate the technologies to local needs
Helps identify agencies and jurisdictions & seeks their participation
COMMUNICATION SYSTEMS
Effective and efficient operation of transit systems relies on a communications infrastructure and vehicle-based communications technologies.
Communications systems are used to transmit voice and data between transit vehicles and operation centers, and to transmit commands between operators and technologies.
Transit communications systems are comprised mostly of wireless technologies and applications.
FLEET MANAGEMENT AND OPERATIONS
These includes separate technologies often are combined in various software packages, which allow for the integration of many different transit functions.
GIS allows transit agencies to accurately track where demand is located in their service area.
APPLICATIONS OF I T S
ELECTRONIC TOLL COLLECTION(E T C)
GLOBAL POSITIONING SYSTEM(G P S)
ADVANCED TRAVELLER INFORMATION SYSTEM(ATIS)
IN-VEHICLE TRANSIT INFORMATION SYSTEM
AUTOMATIC PASSENGER COUNTER
ADVANTAGES OF I T S
Improved safety
Better traffic flow
Lower travel cost
Better environmental quality
Increased business activity
Greater user acceptance
Better travel information
Better planning information
DISADVANTAGES OF I T S
Difficult to use in mixed traffic
Preliminary difficulties in understanding
ITS equipments costly
The control system software could be hacked by hackers
www.wikipedia.com
www.answers.com
www.howstuffworks.com
www.tech-faq.com
www.thetravelinsider.info
http://www.itsoverview.its.dot.
http://www.transport systems.com
http://www.mountain-plains.org
PROGNOSTIC - ADAPTIVE INTELLIGENT DIAGNOSTIC SYSTEM FOR VEHICL.docx
" PROGNOSTIC " - ADAPTIVE INTELLIGENT DIAGNOSTIC SYSTEM FOR VEHICLES
A. A. Poddubnaya, A. V. Keller
FSUE "NAMI", Moscow, Russian Federation
E-mail: [email protected]
Abstract. The article contains general information about promising vehicle diagnostic systems. Existing diagnostic systems, including those built into modern vehicles (TS), are not able to predict the moment of failure of components and assemblies, but only state the fact of a malfunction. To diagnose the current state and forecast the residual life of the vehicle in motion mode, it is proposed to use a mathematical model based on machine learning technologies and data from standard and additional sensors, vehicle detectors. Using this approach will make it possible to forecast the occurrence of a defect before its actual occurrence.
Keywords: advanced diagnostic systems, autonomous vehicle, connected cars, unmanned vehicles, technical condition monitoring, mechanical failure detection, fault prediction, sensors, detectors, digital data processing methods
Introduction
For autonomous transport and connected vehicles, diagnostic of the vehicle’s technical condition is a basic safety standard. * The issue of determining the mechanical failure of an autonomous vehicle is extremely relevant, due to the lack of a driver who can appreciate uncharacteristic noises or external vibrations. Errors received from the vehicle’s CAN bus are not sufficiently informative in assessing the current state of the vehicle and do not predict a breakdown or a failure. For a driverless vehicle, at the stage of its design, an expanded self-diagnosis system should be laid. During operation, onboard the vehicle, data from sensors and a reliability monitoring system should be processed and further data transferred to the ITS - intelligent transport system, as well as to the servers of owners and manufacturers. (* according to researches of the European Commission.)
Main part
Almost all modern cars are modified with a variety of full-time detecting devices and sensors, fixing faults and operation errors of some nodes by electrical parameters and fixing “extreme” system states in codes. Error icons appear on the vehicle dashboard when the system diagnoses a fault. If the driver notes the incorrect operation of certain nodes, systems and you need to make sure in what, really technical condition is the transport, then a specialized diagnosis is carried out. To clarify the technical condition, the computer diagnostics of the vehicle is performed by a certified technical specialist: a scanner with software is connected to the on-board systems, through special diagnostic connectors, CAN, which reads all the codes and errors transmitted by the car about possible malfunctions on the main nodes. Error codes are currently vendor specific, are set by OEM and are available for reading and monitoring in a limited list of codes. The received codes are decrypted by specialists, again using special ...
Smart Traffic Congestion Control System: Leveraging Machine Learning for Urba...
This document proposes a smart traffic congestion control system that leverages machine learning technologies like CNNs, YOLOv4, LSTM, and PPO to optimize traffic flow in urban environments. The system aims to dynamically adjust signal timings in real-time using data analysis and predictive modeling from cameras and sensors. Convolutional neural networks are used for congestion detection from camera images, while YOLOv4 performs object detection to ensure safety. LSTM networks capture temporal traffic data for predictions, and PPO optimizes signal timings based on current conditions. The system has potential to revolutionize traffic management by intelligently reducing congestion through data-driven decision making.
water resources
The document describes a student project to develop an IoT-based traffic signal monitoring and control system. A team of 5 students - A.Deepthi Reddy, A.Guru Sravya, B.Sreya, G.K.Vaishnavi, and K.Shirisha from Vardhaman College of Engineering are working on the project. The system will use sensors to monitor traffic densities at signals and transmit the data online to controllers. It will provide a GUI for controllers to remotely monitor traffic and override signals if needed. The goal is to automate traffic signaling while allowing for manual overrides over the internet.
2
Satellite technology is increasingly being used for traffic management by helping to reduce congestion and enforce traffic laws. It allows for monitoring of traffic through devices like red light cameras, license plate recognition systems, and speed detectors. Electronic toll collection is also facilitated through technologies like RFID and automated vehicle identification. International practices have implemented satellite-based systems for electronic tolling in major cities. Going forward, satellite systems are expected to further optimize traffic control through lower costs and more advanced adaptive systems.
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journal publications
Our journal has been unwavering commitment to showcasing cutting-edge research. The journal provides a platform for researchers to disseminate their work on next-generation technologies. In an era where innovation is the driving force behind progress, JST plays a crucial role in shaping the discourse on emerging technologies, thus contributing to their rapid development and implementation.
IRJET- Image Processing based Intelligent Traffic Control and Monitoring ...
This document summarizes a research paper on an intelligent traffic control and monitoring system using image processing and the Internet of Things. The system aims to reduce traffic congestion by controlling traffic lights based on real-time traffic density detected through image processing of vehicle images. It consists of hardware and software modules. The hardware uses cameras to capture vehicle images and the software uses image processing techniques like object detection and classification to detect and count vehicles in real-time and estimate traffic density. This information is then used to dynamically adjust traffic light timings with the goal of optimizing traffic flow and reducing waiting times at signals. The system is meant to provide a more efficient solution to traffic management than conventional fixed-time traffic light control systems.
Semiconductors in Automotive Industry The Rise of Dynamic PAT and Advanced Ou...
Semiconductors in Automotive Industry The Rise of Dynamic PAT and Advanced Ou...yieldWerx Semiconductor
The automotive industry is undergoing significant transformations in the realm of semiconductor technologies utilized in vehicles. With the increasing number of chips in cars and the growing levels of automation, traditional part average testing (PAT) methods are no longer sufficient to ensure the desired levels of quality and reliability.
While PAT has been a prevalent practice in the automotive sector for nearly three decades, relying on statistical control limits to enhance yield and end-of-the-line quality, the emergence of advanced AI systems and autonomous driving technologies necessitates the adoption of more sophisticated outlier detection techniques and enhanced inspection and test coverage.
The VII propject-V3
This document provides an overview of a graduation project on vehicle infrastructure integration (VII) conducted by a team of students at Ain Shams University in Egypt. The project aims to reduce traffic accidents and monitor traffic by enabling communication between vehicles and between vehicles and roadside infrastructure using wireless technology. It discusses technical issues around communication methods, GPS tracking, microcontrollers and interfaces. It also outlines the system components, hardware, applications including intersection collision avoidance and monitoring systems, and the future work planned for DSRC and wireless communications kits.
Delivering smart-transport-and-traffic-management-solutions
Delivering Smart Transport and Traffic Management Solutions
That Streamline Operations and Enhance Bus Passenger
Travel Experience
developing an_intelligent_tire_concept
This document discusses developing an intelligent tire concept using embedded sensors to provide vehicle control systems with real-time data on tire load, slip angle, friction, and forces. Such a system could enhance vehicle stability and safety. Key challenges include placing sensors in tires during manufacturing to withstand curing temperatures without impacting tire uniformity, and developing an energy harvester to power the sensor system. The proposal is to use modeling, instrumentation, signal processing, and algorithms to estimate variables like friction coefficient and identify slippery road conditions to improve active safety systems.
ACCIDENT DETECTION AND AVOIDANCE USING VEHICLE TO VEHICLE COMMUNICATION (V2V)
The document describes a proposed vehicle accident detection and avoidance system using vehicle-to-vehicle (V2V) communication. The system would use sensors like accelerometers, crash sensors, vibration sensors, alcohol sensors, GPS, and GSM modules to detect accidents and drunk driving in real-time. When an accident or drunk driving incident is detected, the system would send alerts with the vehicle's location to emergency responders. It would also use V2V communication to warn other nearby vehicles of the situation via the NRF24L01 wireless module. The system aims to reduce accidents and save lives by quickly notifying authorities and preventing further collisions. It additionally includes an automated parking feature to safely park a vehicle if drunk driving is detected
Study of Estimation of Road Roughness Condition and Ghat Complexity Analysis ...
This document presents a study on estimating road roughness condition and ghat (mountain pass) complexity using sensors in smartphones. The proposed system utilizes GPS and sensors like accelerometers and magnetometers in Android phones to analyze road bumps and the number of turns in ghats. Data collected from phone sensors is sent to a server for analysis. The server evaluates road conditions and makes information available to users via an app. This allows users to view road conditions and choose alternative routes if needed. Algorithms for detecting road bumps and calculating distance between locations are described. The system aims to provide low-cost continuous monitoring of road infrastructure using smartphones.
Real Time Road Blocker Detection and Distance Calculation for Autonomous Vehi...
Real Time Road Blocker Detection and Distance Calculation for Autonomous Vehi...Associate Professor in VSB Coimbatore
Statistics indicate that most road accidents occur due to a lack of time to react to instant traffic. This problem can be addressed with self-driving vehicles with the application of automated systems to detect such traffic events. The Autonomous Vehicle Navigation System (ATS) has been a standard in the Intelligent Transport System (ITS) and many Driver Assistance Systems (DAS) have been adopted to support these Advanced Autonomous Vehicles (IAVs). To develop these recognition systems for automated self-driving cars, it's important to monitor and operate in real-time traffic events. It requires the correct detection and response of traffic event an automated vehicle. In this paper proposed to develop such a system by applying image recognition to detect and respond to a road blocker by means of real-time distance measurement. To study the performance by measuring accuracy and precision of road blocker detection system and distance calculation, various experiments were conducted by using Shalom frame dataset and detection accuracy, precision of 99%, 100%, while distance calculation 97%, 99% has been achieved by this approach.smart traffic control system using canny edge detection algorithm (4).pdf
The document is a project report submitted by G.Yamini for the partial fulfillment of an MCA degree. It includes a declaration signed by G.Yamini stating that the project titled "SMART TRAFFIC CONTROL SYSTEM USING CANNY EDGE DETECTION ALGORITHM" is her original work and has not been submitted elsewhere. It also includes certificates from the guide and institution confirming that the project is G.Yamini's original work. The project report describes implementing a smart traffic control system using image processing and Canny edge detection algorithm.
Ijetr042176
This document summarizes a research paper that proposes a system to help avoid vehicle collisions and guide parking using vehicular communication technologies. The system uses ultrasonic sensors to detect obstacles and allow vehicles to warn each other of hard braking or crashes to avoid collisions. It also enables emergency vehicles to broadcast their presence so other vehicles can make way. Additionally, infrared sensors help guide vehicles to available parking spaces to reduce traffic congestion from searching for parking. The system was developed as a prototype to integrate these three features and facilitate safer and more efficient driving and parking.
Traffic Signboard Classification with Voice alert to the driver.pptx
The basic idea of proposed system is to provide alertness to the driver about the presence of traffic signboard at a particular distance apart. It generates a warning to the driver in advance of any danger. The warning allows the driver to take appropriate actions in order to avoid the accident.The system takes continuous video input from the console monitor or camera installed on the car's bonnet. The underlying algorithm extracts the features of the input image and matches them with an existing library of traffic sign.
The output is fed to the driving assistance system and in turn drives the car accordingly. We developed this intelligent system using Machine Learning.This device will take camera feeds and upgrade the system
instantaneously.
Density Based Traffic System with Emergency override using Bluetooth
This document is a project report submitted by four students for their bachelor's degree. It outlines a density based traffic system with emergency override using RFID. The proposed system allocates different time slots to roads based on vehicle density to improve time management. It also provides priority traffic signal management for high density lanes and emergency vehicles. An additional feature allows pedestrians to safely cross the road based on their request. The project implements a prototype for an automatic and safer traffic signal management system based on emergency conditions.
VANET BASED SECURED ACCIDENT PREVENTION SYSTEM
This document proposes a notification service to prevent accidents using vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. The system would warn drivers about accidents and hazardous road conditions using messages of different priorities sent between vehicles and roadside units (RSUs). A VANET simulation shows how safety messages reduce driver response time during emergencies. The new communication system improves bandwidth usage for low priority messages containing traffic and weather data shared between RSUs. The simulation results demonstrate that intelligent transportation systems can significantly decrease driver response times and improve road safety.
INTELLIGENT TRANSPORT SYSTEM (ITS)
Why it’s Needed?
Traffic congestion-insufficient road development-growing number of vehicles.
Low speed, increased accident rates, increased fuel consumption, and increased pollution.
Impossible to build enough new roads or to meet the demand.
These explore the concepts that treat highway systems and the vehicles that use them as integrated system. Among them is the concept of Intelligent Transportation Systems.
The goal of I T S is to improve the transportation system to make it more efficient and safer by use of information, communications and control technologies.
India is going through a period of drastic change in the transportation area due to:
Rapidly growing economy.
Insufficient and inadequate public transportation system.
Rising vehicle ownership levels.
ITS PARTS
I T S ARCHITECTURE
· Framework for planning, defining, and integrating intelligent transportation systems.
Benefits of Architecture
Reduces time and resources required to integrate the technologies to local needs
Helps identify agencies and jurisdictions & seeks their participation
COMMUNICATION SYSTEMS
Effective and efficient operation of transit systems relies on a communications infrastructure and vehicle-based communications technologies.
Communications systems are used to transmit voice and data between transit vehicles and operation centers, and to transmit commands between operators and technologies.
Transit communications systems are comprised mostly of wireless technologies and applications.
FLEET MANAGEMENT AND OPERATIONS
These includes separate technologies often are combined in various software packages, which allow for the integration of many different transit functions.
GIS allows transit agencies to accurately track where demand is located in their service area.
APPLICATIONS OF I T S
ELECTRONIC TOLL COLLECTION(E T C)
GLOBAL POSITIONING SYSTEM(G P S)
ADVANCED TRAVELLER INFORMATION SYSTEM(ATIS)
IN-VEHICLE TRANSIT INFORMATION SYSTEM
AUTOMATIC PASSENGER COUNTER
ADVANTAGES OF I T S
Improved safety
Better traffic flow
Lower travel cost
Better environmental quality
Increased business activity
Greater user acceptance
Better travel information
Better planning information
DISADVANTAGES OF I T S
Difficult to use in mixed traffic
Preliminary difficulties in understanding
ITS equipments costly
The control system software could be hacked by hackers
www.wikipedia.com
www.answers.com
www.howstuffworks.com
www.tech-faq.com
www.thetravelinsider.info
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PROGNOSTIC - ADAPTIVE INTELLIGENT DIAGNOSTIC SYSTEM FOR VEHICL.docx
" PROGNOSTIC " - ADAPTIVE INTELLIGENT DIAGNOSTIC SYSTEM FOR VEHICLES
A. A. Poddubnaya, A. V. Keller
FSUE "NAMI", Moscow, Russian Federation
E-mail: [email protected]
Abstract. The article contains general information about promising vehicle diagnostic systems. Existing diagnostic systems, including those built into modern vehicles (TS), are not able to predict the moment of failure of components and assemblies, but only state the fact of a malfunction. To diagnose the current state and forecast the residual life of the vehicle in motion mode, it is proposed to use a mathematical model based on machine learning technologies and data from standard and additional sensors, vehicle detectors. Using this approach will make it possible to forecast the occurrence of a defect before its actual occurrence.
Keywords: advanced diagnostic systems, autonomous vehicle, connected cars, unmanned vehicles, technical condition monitoring, mechanical failure detection, fault prediction, sensors, detectors, digital data processing methods
Introduction
For autonomous transport and connected vehicles, diagnostic of the vehicle’s technical condition is a basic safety standard. * The issue of determining the mechanical failure of an autonomous vehicle is extremely relevant, due to the lack of a driver who can appreciate uncharacteristic noises or external vibrations. Errors received from the vehicle’s CAN bus are not sufficiently informative in assessing the current state of the vehicle and do not predict a breakdown or a failure. For a driverless vehicle, at the stage of its design, an expanded self-diagnosis system should be laid. During operation, onboard the vehicle, data from sensors and a reliability monitoring system should be processed and further data transferred to the ITS - intelligent transport system, as well as to the servers of owners and manufacturers. (* according to researches of the European Commission.)
Main part
Almost all modern cars are modified with a variety of full-time detecting devices and sensors, fixing faults and operation errors of some nodes by electrical parameters and fixing “extreme” system states in codes. Error icons appear on the vehicle dashboard when the system diagnoses a fault. If the driver notes the incorrect operation of certain nodes, systems and you need to make sure in what, really technical condition is the transport, then a specialized diagnosis is carried out. To clarify the technical condition, the computer diagnostics of the vehicle is performed by a certified technical specialist: a scanner with software is connected to the on-board systems, through special diagnostic connectors, CAN, which reads all the codes and errors transmitted by the car about possible malfunctions on the main nodes. Error codes are currently vendor specific, are set by OEM and are available for reading and monitoring in a limited list of codes. The received codes are decrypted by specialists, again using special ...
Smart Traffic Congestion Control System: Leveraging Machine Learning for Urba...
This document proposes a smart traffic congestion control system that leverages machine learning technologies like CNNs, YOLOv4, LSTM, and PPO to optimize traffic flow in urban environments. The system aims to dynamically adjust signal timings in real-time using data analysis and predictive modeling from cameras and sensors. Convolutional neural networks are used for congestion detection from camera images, while YOLOv4 performs object detection to ensure safety. LSTM networks capture temporal traffic data for predictions, and PPO optimizes signal timings based on current conditions. The system has potential to revolutionize traffic management by intelligently reducing congestion through data-driven decision making.
water resources
The document describes a student project to develop an IoT-based traffic signal monitoring and control system. A team of 5 students - A.Deepthi Reddy, A.Guru Sravya, B.Sreya, G.K.Vaishnavi, and K.Shirisha from Vardhaman College of Engineering are working on the project. The system will use sensors to monitor traffic densities at signals and transmit the data online to controllers. It will provide a GUI for controllers to remotely monitor traffic and override signals if needed. The goal is to automate traffic signaling while allowing for manual overrides over the internet.
2
Satellite technology is increasingly being used for traffic management by helping to reduce congestion and enforce traffic laws. It allows for monitoring of traffic through devices like red light cameras, license plate recognition systems, and speed detectors. Electronic toll collection is also facilitated through technologies like RFID and automated vehicle identification. International practices have implemented satellite-based systems for electronic tolling in major cities. Going forward, satellite systems are expected to further optimize traffic control through lower costs and more advanced adaptive systems.
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PROGNOSTIC - ADAPTIVE INTELLIGENT DIAGNOSTIC SYSTEM FOR VEHICL.docx
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- 1. Enhancing Road Safety Through Advanced Lane Line Detection Technology
- 2. Introduction This presentation will explore the impact of advanced lane line detection technology on road safety.We will discuss the challenges of traditional lane detection and the potential of advanced systems. Join us in this exploration of cutting-edge technology.
- 3. Inadequate lane detection systems contribute to accidents and traffic congestion. Traditional lane markings are often obscured by weather conditions or poor visibility, posing significant risks to drivers. Advanced detection technology aims to address these challenges.
- 4. Advanced lane line detection technology utilizes computer vision and machine learning algorithms to accurately identify lane markings.By integrating sensor data and real-time analysis,these systems enhance precision and reliability, contributing to safer roads. Understanding Advanced Lane Line Detection
- 5. Enhanced lane detection technology offers improved accuracy, enabling vehicles to maintain optimal positioning within lanes. This contributes to a significant reduction in lane departure incidents and supports efficient traffic flow.
- 6. While advanced lane detection technology presents significant benefits, challenges such as adverse weather conditions and obscured lane markings can impact its effectiveness.Understanding these limitations is crucial for further development. Challenges and Limitations
- 7. Advanced lane line detection technology plays a vital role in the development of autonomous vehicles. By providing precise lane information, these systems contribute to the safe navigation and decision-making processes of autonomous vehicles.
- 8. Ongoing research and innovations in lane line detection technology aim to address current limitations and further enhance reliability. The future holds promising advancements in real-time analysis and adaptive systems.
- 9. Real-world case studies demonstrate the tangible impact of advanced lane detection technology on reducing accidents and improving overall road safety.These success stories highlight the potential for widespread implementation. Case Studies and Success Stories
- 10. As advanced lane detection technology becomes more prevalent,regulatory bodies are tasked with establishing standards and guidelines for its implementation.These considerations are essential for ensuring uniform safety standards across road networks. Regulatory Considerations
- 11. Collaboration between automotive manufacturers,technology developers, and regulatory bodies is crucial for the successful integration of advanced lane detection technology. These partnerships drive innovation and promote industry- wide adoption. Industry Collaboration and Partnerships
- 12. In conclusion, advanced lane line detection technology represents a significant advancement in enhancing road safety.By addressing traditional challenges and offering precise detection, these systems contribute to the reduction of accidents and support the evolution towards safer and more efficient road networks. Conclusion
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