While NFV and SDN have showcases their potential in cloud Data centers, experts are looking to bring its expertise for creating a secured safer smart ride through the integration of vehicle-vehicle and vehicle-infrastructure communications which create smart locales. Today we have understood the requirements and networking involved to realize centralized and distributed clouds to support customer premise services and IIoT. But we have a partial gain from these technologies. To unlock the real potential of Edge networks, the Automotive industry is moving towards integrating ADAS and intelligent roadside infrastructure with Cloud Edge and NFV technologies to create a Safer and Smarter Ride.
This presentation showcases on NFV for Automotive to create safer and smart ride.
Automotive Cybersecurity Challenges for Automated Vehicles: Jonathan PetitSecurity Innovation
July 2016: Jonathan Petit, Principal Scientist at Security Innovation, discusses cybersecurity challenges for automated vehicles at the Automotive Vehicles Symposium.
Automated Highway System (AHS) is an example of a large-scale, multi-agent, hybrid dynamical system. In this paper, the use of computer aided simulation tool for design and evaluation of control laws, for an AHS based on platooning, is outlined.
automated highway system ppt
truck platooning systems
automated driving system demonstration grant
accident on hwy 74 today
what is platooning of trucks
autonomous vehicles platooning
vehicle platooning
hwy 58 traffic report
interesting civil engineering topics
seminar topics pdf
civil engineering topics for presentation
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civil engineering ppt
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Vehicle to vehicle Communication Systems (V2V) are an emerging type of networks in which vehicles use a dynamic wireless exchange of data between nearby vehicles providing each other with information, such as safety warnings and traffic information.
Connected vehicles are coming soon to a road near you and according to U.S. Department of Transportation, these "talking cars" can have the potential to prevent 80% car crashes. But did you know that connected vehicles can also keep pedestrians safe too. Learn more at our session at SXSW 2017.
Drive Oregon Event: Connected Cars: The Future of TransportationForth
Drive Oregon's September 2013 event featured Dr. Robert Bertini speaking on the the benefits of "connected car" technology.
In December 2012, Governor Kitzhaber released the 10 Year Energy Plan, a bold roadmap forward aimed at reducing our state’s energy usage. Improving and expanding our state’s intelligent transportation system, which relies on “smart” or “connected” technology, was included in the plan as an integral step toward increasing the efficiency and safety of our roads.
Dr. Bertini's presentation gives a great overview of what the future of Oregon's roads will probably look like!
(Paper) A Method for Sharing Traffic Jam Information using Inter-Vehicle Comm...Naoki Shibata
Shibata, N., Terauchi, T., Kitani, T., Yasumoto, K., Ito, M., Higashino, T.: A Method for Sharing Traffic Jam Information Using Inter-Vehicle Communication, The 2nd International Workshop on Vehicle-to-Vehicle Communications (V2VCOM) (Mobiquitous2006 Workshop), pp. 1-7, DOI:10.1109/MOBIQ.2006.340428 (July 2006) (invited paper).
http://ito-lab.naist.jp/themes/pdffiles/060725.shibata.v2vcom06.pdf
In this paper, we propose a method for cars to autonomously and cooperatively collect traffic jam statistics to estimate arrival time to destination for each car using inter-vehicle communication. In the method, the target geographical region is divided into areas, and each car measures time to pass through each area. Traffic information is collected by exchanging information between cars using inter-vehicle communication. In order to improve accuracy of estimation, we introduce several mechanisms to avoid same data to be repeatedly counted. Since wireless bandwidth usable for exchanging statistics information is limited, the proposed method includes a mechanism to categorize data, and send important data prior to other data. In order to evaluate effectiveness of the proposed method, we implemented the method on a traffic simulator NETSTREAM developed by Toyota Central R&D Labs, conducted some experiments and confirmed that the method achieves practical performance in sharing traffic jam information using inter-vehicle communication.
Vehicle-2-Vehicle Communication Based on Wireless Sensor NetworkjournalBEEI
Truck Platooning is a car innovation that permits gathering various trucks into a single element where one truck intently takes after the other that outcomes in an expanded street limit. This kind of detachment allows to a significant degree tight separations and synchronous driving between the vehicles. Our point is to plan and exhibit a self-ruling truck platooning framework given vehicle-to-vehicle (V2V) correspondence innovation. The structure utilises IEEE 802.15.4 remote convention joined with separation going sensors to enable vehicles inside the company to safely trade data progressively and naturally break and quicken in light of the lead truck. The rapid of remote correspondence permits to a significant degree tight separations and synchronous driving between the platooning vehicles.
Implementing Secured and Comport Transportation using Vehicular Ad-Hoc Networ...ijtsrd
VANET is the largest wireless communications research area. VANETs of rapidly moving vehicles can be inefficient or unreliable. With the passing of time, VANET technology advances via inter vehicle interaction, but many problems need to be resolved in order to strengthen the network. This paper simulates road traffic simulators in a way that ensures safe communication between different types vehicles and prevents traffic based congestion in the cities of India. Ms. Pooja Deshpande | Mrs. Vrushali Uttarwar | Ms. Ekta Choudhari "Implementing Secured and Comport Transportation using Vehicular Ad-Hoc Network for Realistic City Scenario" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29771.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/29771/implementing-secured-and-comport-transportation-using-vehicular-ad-hoc-network-for-realistic-city-scenario/ms-pooja-deshpande
The idea of Intelligent Transportation Systems (ITS) is utilized when discussing correspondence advancements among vehicles and framework to improve, among others, street wellbeing. We propose a notice administration to avoid mishaps by cautioning drivers about mishaps and perilous street conditions. This administration incorporates the meaning of another communicate dispersal system. A VANET roadway situation is mimicked to assess how the utilization of wellbeing plans diminishes the driver's response time when a startling circumstance happens. This new administration incorporates the meaning of another communicates spread component for low need messages that improve the data transfer capacity utilization. The end drawn in the wake of mimicking the shrewd street structure is that the utilization of astute foundation definitely decreases the response time of the driver. This will deliver an improvement in transport wellbeing since a vehicle would require less space to maintain a strategic distance from a surprising circumstance contrasted with not utilizing these advancements
Cisco Smart Intersections: IoT insights using video analytics and AICarl Jackson
In this trial, IoT, Video Analytics, Deep Learning (DL) and Artificial Intelligence (AI), for the purpose of traffic flow assessment and insights into road user behaviour, were evaluated at an intersection at the AIMES testbed in Melbourne¹ in partnership with: the University of Melbourne, Department of Transport (DOT), IAG and Cisco.
Inter vehicular communication
Inter vehicular communication
Inter vehicular communication
Inter vehicular communication
Inter vehicular communication
Inter vehicular communication
Inter vehicular communication
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
With the recent advancement of technology, a new technology named VANET (Vehicular Adhoc
Network) is emerging day by day. VANET is a wireless communication between vehicles to vehicles
and RSU (vehicles to road side units). It is different from MANET, so the challenges of VANET are also
different from MANET. It has many challenges like safety, traffic and user application based challenges
which require some particular design. The vehicular mobility model plays a vital role in examining
different challenges. There are different models for different purposes and for getting better results we
have to apply the correct model which is suitable for the particular situation. In this paper, a proper
classification is done between different vehicular mobility models with respect to their types, sub types,
usage (interaction level), evaluating purpose and example of each model is also provided
Welcome to the Connected Vehicle Training Overview. This program will give professionals an overview of overarching concepts of the connected vehicle space Mobile Comply has created the Connected Vehicle Management Overview, a highly selective two-hour course designed to give participants a basic understanding of the connected vehicle space for Future connected vehicle education and certification programs.
Automotive Cybersecurity Challenges for Automated Vehicles: Jonathan PetitSecurity Innovation
July 2016: Jonathan Petit, Principal Scientist at Security Innovation, discusses cybersecurity challenges for automated vehicles at the Automotive Vehicles Symposium.
Automated Highway System (AHS) is an example of a large-scale, multi-agent, hybrid dynamical system. In this paper, the use of computer aided simulation tool for design and evaluation of control laws, for an AHS based on platooning, is outlined.
automated highway system ppt
truck platooning systems
automated driving system demonstration grant
accident on hwy 74 today
what is platooning of trucks
autonomous vehicles platooning
vehicle platooning
hwy 58 traffic report
interesting civil engineering topics
seminar topics pdf
civil engineering topics for presentation
civil seminar topics ppt
best seminar topics for civil engineering
seminar topics for mechanical engineers
civil engineering ppt
latest civil engineering seminar topics
Vehicle to vehicle Communication Systems (V2V) are an emerging type of networks in which vehicles use a dynamic wireless exchange of data between nearby vehicles providing each other with information, such as safety warnings and traffic information.
Connected vehicles are coming soon to a road near you and according to U.S. Department of Transportation, these "talking cars" can have the potential to prevent 80% car crashes. But did you know that connected vehicles can also keep pedestrians safe too. Learn more at our session at SXSW 2017.
Drive Oregon Event: Connected Cars: The Future of TransportationForth
Drive Oregon's September 2013 event featured Dr. Robert Bertini speaking on the the benefits of "connected car" technology.
In December 2012, Governor Kitzhaber released the 10 Year Energy Plan, a bold roadmap forward aimed at reducing our state’s energy usage. Improving and expanding our state’s intelligent transportation system, which relies on “smart” or “connected” technology, was included in the plan as an integral step toward increasing the efficiency and safety of our roads.
Dr. Bertini's presentation gives a great overview of what the future of Oregon's roads will probably look like!
(Paper) A Method for Sharing Traffic Jam Information using Inter-Vehicle Comm...Naoki Shibata
Shibata, N., Terauchi, T., Kitani, T., Yasumoto, K., Ito, M., Higashino, T.: A Method for Sharing Traffic Jam Information Using Inter-Vehicle Communication, The 2nd International Workshop on Vehicle-to-Vehicle Communications (V2VCOM) (Mobiquitous2006 Workshop), pp. 1-7, DOI:10.1109/MOBIQ.2006.340428 (July 2006) (invited paper).
http://ito-lab.naist.jp/themes/pdffiles/060725.shibata.v2vcom06.pdf
In this paper, we propose a method for cars to autonomously and cooperatively collect traffic jam statistics to estimate arrival time to destination for each car using inter-vehicle communication. In the method, the target geographical region is divided into areas, and each car measures time to pass through each area. Traffic information is collected by exchanging information between cars using inter-vehicle communication. In order to improve accuracy of estimation, we introduce several mechanisms to avoid same data to be repeatedly counted. Since wireless bandwidth usable for exchanging statistics information is limited, the proposed method includes a mechanism to categorize data, and send important data prior to other data. In order to evaluate effectiveness of the proposed method, we implemented the method on a traffic simulator NETSTREAM developed by Toyota Central R&D Labs, conducted some experiments and confirmed that the method achieves practical performance in sharing traffic jam information using inter-vehicle communication.
Vehicle-2-Vehicle Communication Based on Wireless Sensor NetworkjournalBEEI
Truck Platooning is a car innovation that permits gathering various trucks into a single element where one truck intently takes after the other that outcomes in an expanded street limit. This kind of detachment allows to a significant degree tight separations and synchronous driving between the vehicles. Our point is to plan and exhibit a self-ruling truck platooning framework given vehicle-to-vehicle (V2V) correspondence innovation. The structure utilises IEEE 802.15.4 remote convention joined with separation going sensors to enable vehicles inside the company to safely trade data progressively and naturally break and quicken in light of the lead truck. The rapid of remote correspondence permits to a significant degree tight separations and synchronous driving between the platooning vehicles.
Implementing Secured and Comport Transportation using Vehicular Ad-Hoc Networ...ijtsrd
VANET is the largest wireless communications research area. VANETs of rapidly moving vehicles can be inefficient or unreliable. With the passing of time, VANET technology advances via inter vehicle interaction, but many problems need to be resolved in order to strengthen the network. This paper simulates road traffic simulators in a way that ensures safe communication between different types vehicles and prevents traffic based congestion in the cities of India. Ms. Pooja Deshpande | Mrs. Vrushali Uttarwar | Ms. Ekta Choudhari "Implementing Secured and Comport Transportation using Vehicular Ad-Hoc Network for Realistic City Scenario" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29771.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/29771/implementing-secured-and-comport-transportation-using-vehicular-ad-hoc-network-for-realistic-city-scenario/ms-pooja-deshpande
The idea of Intelligent Transportation Systems (ITS) is utilized when discussing correspondence advancements among vehicles and framework to improve, among others, street wellbeing. We propose a notice administration to avoid mishaps by cautioning drivers about mishaps and perilous street conditions. This administration incorporates the meaning of another communicate dispersal system. A VANET roadway situation is mimicked to assess how the utilization of wellbeing plans diminishes the driver's response time when a startling circumstance happens. This new administration incorporates the meaning of another communicates spread component for low need messages that improve the data transfer capacity utilization. The end drawn in the wake of mimicking the shrewd street structure is that the utilization of astute foundation definitely decreases the response time of the driver. This will deliver an improvement in transport wellbeing since a vehicle would require less space to maintain a strategic distance from a surprising circumstance contrasted with not utilizing these advancements
Cisco Smart Intersections: IoT insights using video analytics and AICarl Jackson
In this trial, IoT, Video Analytics, Deep Learning (DL) and Artificial Intelligence (AI), for the purpose of traffic flow assessment and insights into road user behaviour, were evaluated at an intersection at the AIMES testbed in Melbourne¹ in partnership with: the University of Melbourne, Department of Transport (DOT), IAG and Cisco.
Inter vehicular communication
Inter vehicular communication
Inter vehicular communication
Inter vehicular communication
Inter vehicular communication
Inter vehicular communication
Inter vehicular communication
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
With the recent advancement of technology, a new technology named VANET (Vehicular Adhoc
Network) is emerging day by day. VANET is a wireless communication between vehicles to vehicles
and RSU (vehicles to road side units). It is different from MANET, so the challenges of VANET are also
different from MANET. It has many challenges like safety, traffic and user application based challenges
which require some particular design. The vehicular mobility model plays a vital role in examining
different challenges. There are different models for different purposes and for getting better results we
have to apply the correct model which is suitable for the particular situation. In this paper, a proper
classification is done between different vehicular mobility models with respect to their types, sub types,
usage (interaction level), evaluating purpose and example of each model is also provided
Welcome to the Connected Vehicle Training Overview. This program will give professionals an overview of overarching concepts of the connected vehicle space Mobile Comply has created the Connected Vehicle Management Overview, a highly selective two-hour course designed to give participants a basic understanding of the connected vehicle space for Future connected vehicle education and certification programs.
Autonomous Vehicles: Technologies, Economics, and OpportunitiesJeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how the cost and performance of autonomous vehicles are improving rapidly. LIDAR, other sensors, ICs, and wireless are experiencing rapid improvements that are enabling the overall cost of AVs to fall. For example, the latency of wireless systems is improving rapidly thus enabling vehicles to be controlled with wireless systems. This is also creating many new opportunities in the vehicle industry in the Internet of Things, data analytics, and logistics. The slides include a detailed discussion of AVs in Singapore, a likely early adopter.
Automatic control systems related to safety in autonomous carsMRUGENDRASHILVANT
Various technologies used in the Safety of the Autonomous vehicles are discussed. These techniques are explained with the help of various simple examples.
All throughout APAC the landscape is changing and presenting a need for smart mobility. Read more in detail to learn how businesses can seize opportunities with the right IT strategy and the right partnership.
Internet for vanet network communications fleetnetIJCNCJournal
Now in the world, the exchange of information between vehicles in the roads without any fixed infrastructure is enabled thanks to the novel technology of the Vehicular adhoc networks called (VANETs).The accidents and congestions warning, Internet access e.g. via gateways along the road are the main applications of these networks related to the safety and comfort applications. A high requirement on the routing protocols is introduced in these complexed VANETs networks In order to implement a reference intelligent transportation system and contribute to the standardization of vehicle to vehicle communication or vehicle to infrastructure, in Europe, several projects are held and different partners are joined from the industry, governmental agencies and academia.This paper explains the main progress and purposes of the standardization process and research initiatives of FleetNet project. These solutions will present in the future a common worldwide VANET platform integrating several services of inter-vehicles communications.
Smart infrastructure for autonomous vehicles Jeffrey Funk
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how autonomous vehicles are becoming economic feasible. They are becoming economically feasible because the cost of lasers, ICs, MEMS, and other electronic components are falling at 25 to 40% per year. If the cost of autonomous vehicles fall 25% a year, the cost of the electronics associated with autonomous vehicles will fall 90% in 10 years. Dedicating roads to autonomous vehicles is necessary to achieve the most benefits from autonomous vehicles. While using autonomous vehicles in combination with conventional vehicles can free drivers for other activities, dedicating roads to autonomous vehicles can dramatically reduce congestion, increase speeds, and thus increase the number of cars per area of the road. They can also reduce accidents, insurance, and the number of traffic police. These slide discuss the use of wireless technologies for the control and coordination of autonomous vehicles. Improvements in bandwidth, speed, and latency (delays) along with improvements in computer processing are occurring and these improvements are making dedicated roads for autonomous vehicles economically feasible.
types of modern technologies used in transportation, uses of modern technology in transportation ,Introduction
Why ITS?
Application of ITS
Implementation of ITS
Benefits of ITS
Demerits of ITS
OpenStack and Kubernetes - A match made for Telco HeavenTrinath Somanchi
With the advent of Containerization of Telco Clouds for NFV and SDN based deployments, OpenStack with Kubernetes is a best chosen option to solve the challenges is a better way to build a containerized Telco cloud. This involves, "Kubernetes in OpenStack", "OpenStack in Kubernetes" and "Independent OpenStack and Kubernetes". With this complementing collaboration, in the Stadium of OpenStack's Open Infrastructure, Telecom gaints are developing cloud-native solutions to best fit the next generation networking deployments. In this Presentation, we talk about Containerization and benefits, OpenStack and Kubernetes match making and we give a brief overview on Airship and Kata Container projects.
SDN and NFV integrated OpenStack Cloud - Birds eye view on SecurityTrinath Somanchi
Network security and reliability are the most challenging tasks in any cloud. With NFV and SDN in place, Network Functions are virtualzied and network traffic is managed in separated control and data planes. Thus reducing the operational and capital expenditure. Virtualized Network Functions are tied with Software Defined Networks to boost the power of virtualization.
This itself is challenging when Network services and security is a concern. While OpenStack is the best opted solution for IaaS, many service provides are moving towards best solutions to deal with service delivery and security challenges in SDN and NFV integrated OpenStack Cloud.
OpenStack Collaboration made in heaven with Heat, Mistral, Neutron and more..Trinath Somanchi
Cross-project collaboration is something OpenStack community has embraced for a long time. Common libraries like Oslo reduces the time and effort to build a new service. Another way this manifests is in new OpenStack services getting built using existing services to solve an higher level use-case.
In this talk we are present how the band of projects comprising of Mistral, Tacker, Neutron, Heat, TOSCA-parser and Barbican came together to build an industry leading ETSI NFV Orchestrator that leveraged the best of these projects. Each of these projects brought in critical functionalities needed towards the final product. You will learn how, when strung together, this solution follows the classic Microservices design pattern that the industry is rapidly adopting.
Securing NFV and SDN Integrated OpenStack Cloud: Challenges and SolutionsTrinath Somanchi
Network security and reliability are the most challenging tasks in any cloud. With NFV and SDN in place, Network Functions are virtualzied and network traffic is managed in separated control and data planes. Thus reducing the operational and capital expenditure. Virtualized Network Functions are tied with Software Defined Networks to boost the power of virtualization. This itself is challenging when Network services and security is a concern. While OpenStack is the best opted solution for IaaS, many service provides are moving towards best solutions to deal with service delivery and security challenges in SDN and NFV integrated OpenStack Cloud.
The Presentation outlines the challenges and proposes probable solutions for NFV and SDN integrated OpenStack Cloud.
Distributed VNF Management - Architecture and Use casesTrinath Somanchi
Telco operators are on journey to discover what virtualization means for the network. Markets have believed that NFV architecture elements: NFVI and VIM, hold the complete responsibility in providing virtualized networks with carrier grade properties.
Telco operators have reached to a conclusion that VNFs must take their fair share of responsibility to realize NFV goals while meeting carrier-grade behavior in the entire NFV architecture. While the trend moves on, Cloud native VNFs are emerging best citizens of the cloud. Thus communication from EMS to VNFM is blurred and eventually may disappear in the future. This requires better understanding of, and agreement over the role of VNFMs and EMS for VNFs.
This presentation describes the evolution of Distributed VNF management, Architectural design considerations and Use-case scenarios. The following proposal is based on a comprehensive study on evolving cloud native VNF management.
Virtual Network Function Managers (VNFM) are Key components in NFV MANO framework. They work in concert with Network Function Virtualization Orchestrator (NFVO) and Virtual Infrastructure Manager (VIM).In this presentation, We will compare competing Opensource VNFMs with respect to various features supported.
Your VW's camshaft position sensor is crucial for engine performance. Signs of failure include engine misfires, difficulty starting, stalling at low speeds, reduced fuel efficiency, and the check engine light. Prompt inspection and replacement can prevent further damage and keep your VW running smoothly.
Comprehensive program for Agricultural Finance, the Automotive Sector, and Empowerment . We will define the full scope and provide a detailed two-week plan for identifying strategic partners in each area within Limpopo, including target areas.:
1. Agricultural : Supporting Primary and Secondary Agriculture
• Scope: Provide support solutions to enhance agricultural productivity and sustainability.
• Target Areas: Polokwane, Tzaneen, Thohoyandou, Makhado, and Giyani.
2. Automotive Sector: Partnerships with Mechanics and Panel Beater Shops
• Scope: Develop collaborations with automotive service providers to improve service quality and business operations.
• Target Areas: Polokwane, Lephalale, Mokopane, Phalaborwa, and Bela-Bela.
3. Empowerment : Focusing on Women Empowerment
• Scope: Provide business support support and training to women-owned businesses, promoting economic inclusion.
• Target Areas: Polokwane, Thohoyandou, Musina, Burgersfort, and Louis Trichardt.
We will also prioritize Industrial Economic Zone areas and their priorities.
Sign up on https://profilesmes.online/welcome/
To be eligible:
1. You must have a registered business and operate in Limpopo
2. Generate revenue
3. Sectors : Agriculture ( primary and secondary) and Automative
Women and Youth are encouraged to apply even if you don't fall in those sectors.
What Could Cause The Headlights On Your Porsche 911 To Stop WorkingLancer Service
Discover why your Porsche 911 headlights might flicker out unexpectedly. From aging bulbs to electrical gremlins and moisture mishaps, we're delving into the reasons behind the blackout. Stay tuned to illuminate the road ahead and ensure your lights shine bright for safer journeys.
Ever been troubled by the blinking sign and didn’t know what to do?
Here’s a handy guide to dashboard symbols so that you’ll never be confused again!
Save them for later and save the trouble!
Why Is Your BMW X3 Hood Not Responding To Release CommandsDart Auto
Experiencing difficulty opening your BMW X3's hood? This guide explores potential issues like mechanical obstruction, hood release mechanism failure, electrical problems, and emergency release malfunctions. Troubleshooting tips include basic checks, clearing obstructions, applying pressure, and using the emergency release.
"Trans Failsafe Prog" on your BMW X5 indicates potential transmission issues requiring immediate action. This safety feature activates in response to abnormalities like low fluid levels, leaks, faulty sensors, electrical or mechanical failures, and overheating.
Symptoms like intermittent starting and key recognition errors signal potential problems with your Mercedes’ EIS. Use diagnostic steps like error code checks and spare key tests. Professional diagnosis and solutions like EIS replacement ensure safe driving. Consult a qualified technician for accurate diagnosis and repair.
The Octavia range embodies the design trend of the Škoda brand: a fusion of
aesthetics, safety and practicality. Whether you see the car as a whole or step
closer and explore its unique features, the Octavia range radiates with the
harmony of functionality and emotion
Learn why monitoring your Mercedes' Exhaust Back Pressure (EBP) sensor is crucial. Understand its role in engine performance and emission reduction. Discover five warning signs of EBP sensor failure, from loss of power to increased emissions. Take action promptly to avoid costly repairs and maintain your Mercedes' reliability and efficiency.
𝘼𝙣𝙩𝙞𝙦𝙪𝙚 𝙋𝙡𝙖𝙨𝙩𝙞𝙘 𝙏𝙧𝙖𝙙𝙚𝙧𝙨 𝙞𝙨 𝙫𝙚𝙧𝙮 𝙛𝙖𝙢𝙤𝙪𝙨 𝙛𝙤𝙧 𝙢𝙖𝙣𝙪𝙛𝙖𝙘𝙩𝙪𝙧𝙞𝙣𝙜 𝙩𝙝𝙚𝙞𝙧 𝙥𝙧𝙤𝙙𝙪𝙘𝙩𝙨. 𝙒𝙚 𝙝𝙖𝙫𝙚 𝙖𝙡𝙡 𝙩𝙝𝙚 𝙥𝙡𝙖𝙨𝙩𝙞𝙘 𝙜𝙧𝙖𝙣𝙪𝙡𝙚𝙨 𝙪𝙨𝙚𝙙 𝙞𝙣 𝙖𝙪𝙩𝙤𝙢𝙤𝙩𝙞𝙫𝙚 𝙖𝙣𝙙 𝙖𝙪𝙩𝙤 𝙥𝙖𝙧𝙩𝙨 𝙖𝙣𝙙 𝙖𝙡𝙡 𝙩𝙝𝙚 𝙛𝙖𝙢𝙤𝙪𝙨 𝙘𝙤𝙢𝙥𝙖𝙣𝙞𝙚𝙨 𝙗𝙪𝙮 𝙩𝙝𝙚 𝙜𝙧𝙖𝙣𝙪𝙡𝙚𝙨 𝙛𝙧𝙤𝙢 𝙪𝙨.
Over the 10 years, we have gained a strong foothold in the market due to our range's high quality, competitive prices, and time-lined delivery schedules.
Welcome to ASP Cranes, your trusted partner for crane solutions in Raipur, Chhattisgarh! With years of experience and a commitment to excellence, we offer a comprehensive range of crane services tailored to meet your lifting and material handling needs.
At ASP Cranes, we understand the importance of reliable and efficient crane operations in various industries, from construction and manufacturing to logistics and infrastructure development. That's why we strive to deliver top-notch solutions that enhance productivity, safety, and cost-effectiveness for our clients.
Our services include:
Crane Rental: Whether you need a crawler crane for heavy lifting or a hydraulic crane for versatile operations, we have a diverse fleet of well-maintained cranes available for rent. Our rental options are flexible and can be customized to suit your project requirements.
Crane Sales: Looking to invest in a crane for your business? We offer a wide selection of new and used cranes from leading manufacturers, ensuring you find the perfect equipment to match your needs and budget.
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TRANSFORMER OIL classifications and specifications
Creating a Safer, Smarter ride - NFV for Automotive
1. NXP DIGITAL NETWORKING GROUP
STEVE FURR, TRINATH SOMANCHI, NELSON YANG
CREATING A SAFER, SMARTER
RIDE – NFV FOR AUTOMOTIVE
MARCH, 2018
2. 1
AGENDA
• Motivation
• Major Causes of Collisions and Injuries
• Communication Needs
• Architecture for ITS Edge Computing
• Use Cases
• NXP Solutions
4. 3
Source: National Highway Transportation Safety Administration
• In 2016, 37,461 people died in motor
vehicle crashes
• Costing > $650B annually
• Motor vehicle accidents are the
leading cause of death among youth
and young adults 16-24
• The vast number of vehicle crashes
are tied to human error
• Everyone is a pedestrian
• On average, a pedestrian is killed
every two hours and injured every
seven minutes in traffic crashes
• Fourteen percent of all traffic
fatalities and an estimated 3 percent
of those are injured in traffic
crashes are pedestrians
More than 70% of all fatal collisions involve speeding or aggressive driving as a factor
Vehicle Safety Pedestrian Safety
Motivation – Improve Traffic Safety
5. 4
Sources: Federal Highway Administration, American Society of Civil Engineers, EU 5G Public-private partnership (5G-PPP)
Capability trap - need to achieve more with
existing (deteriorating) road infrastructure
• Road capacity and quality levels haven’t
improved significantly in several years
• Rehabilitation and maintenance have
expenditures increasing at exponential rates
• Government agencies in difficult financial
situation asking for more resources
• No room or desire to build out roadways
Increase capacity of existing roadways
• Network density and scale – 5G network
infrastructure will see:
− 1,000x increase in mobile data volume per
geographical area ≥ 10 Tb/s/km2
− 1,000x increase device density ≥ 10 1M/km2
− 1/5x end-to-end latency, reaching target ≤
1ms for vehicle-to-vehicle communications
− Accuracy of outdoor terminal location < 1m
• Limited availability of suitable sites for
basestation placement to achieve density
Public-private partnerships for 5G network infrastructure leverage disruption to revolutionize ITS
by locating edge applications at private facilities in the public realm
Transportation Infrastructure Gap 5G Network Infrastructure
Motivation – Synergize PPPs for 5G Build-out
7. 6
Proximate Causes of Fatal Crashes
Driving on wrong side of road
Careless driving
Operating with improper equipment
Improper turn
Failure to yield
Overcorrection
Failure to adjust to conditions
Failure to keep to proper lane
Alert drivers to the bad behaviors causing fatal crashes
Source: AutoInsurance Center
8. 7
Roadmap to Autonomous Passenger Vehicles
• Roadmap to fully autonomous
operation is more than a decade
• Autonomous vehicles will continue to
share the road with “legacy” users
for a long period
Source: European Road Transport Research Advisory Council
Intelligent transportation will rely
heavily on “assistive” technologies
enhancing situational awareness
12. 11
Connected & Automated Vehicle (CAV) Communications Needs
Vehicle to Vehicle (V2V)
• Short range communications
• Allows similarly equipped vehicles to signal intent
− Wireless turn signal
− Wireless brake light
− Intent to merge but remain in lane
− Intent to merge into your lane
− Relay emergency braking message from vehicles ahead
• Without V2V, CAVs must drive very conservatively.
• Vehicle to Infrastructure (V2I)
• DSRC (short range) or Cellular (short & long) range
communications
• Helps CAV and legacy vehicles navigate complex
environments more smoothly
− Intelligent traffic lights; flow control based on volume
− Supplemental navigation signals
− Merger of Tolls with prioritized routing
• High security and QoS requirements to isolate safety
messages from high bandwidth non-navigational infotainment
13. 12
Use Cases
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04.
14. 13
Forward Collision Prevention
Preventing Detectable Collisions
Forward Collision Warning
Detects a potential collision and
warns the driver
Hard Braking Ahead
Detects an obstruction and
warns the driver, automatically
reducing speed
Automated Emergency Braking
Applies brakes when forward
collision imminent
Collision Avoidance
Detects an imminent collision
and navigates to avoid
Pedestrian Automated
Emergency Braking
Detects pedestrian, warns driver
and automatically brakes if
collision imminent
Lateral Threat Detection
Warns of: emergency vehicle,
unyielding vehicle, etc., and
automatically brakes if collision
imminent
Assistive Edge-Enhanced Situational Awareness
15. 14
Lane Navigation
Navigating Safely Around Other Vehicles
Lane Departure Warning
Monitors lanes and provides
warnings if driver is out of bounds
Lane Following
Assists driver following lane,
including in adverse conditions
Lane Keeping Assist
Helps driver stay within bounds of
lane
Aggressive Driver Warning
Detects a vehicle speeding or
maneuvering aggressively and
warns the driver
Blind Spot Detection
Warns of vehicle in driver’s blind
spot (when turning)
Collision Avoidance
Detects impending blind spot
encroachment and navigates to
avoid
Assistive Edge-Enhanced Situational Awareness
Map (guideposts)
16. 15
Safe Separation
Maintaining Safe Following Distances
Traffic Jam Assist
Automatically accelerates and
brakes the vehicle with the flow of
traffic
Convoying / Platooning
A convoy of vehicles follows a
lead car to achieve efficiency
Highway Pilot
Maintains vehicle’s lane position
and following distance by braking
and accelerating as needed
Congestion Avoidance
Adaptive Cruise Control
Automatically adjusts vehicle’s
speed to keep a preset distance
from the vehicle in front
Assistive Edge-Enhanced Situational Awareness
reroute
17. 16
CLOUD
Back Haul
to the Cloud
Broadband
Hotspot
Legacy
Vehicle
Detection
Pedestrian
(VRU) Detection
Obstruction
DSRC Relay
VRU Warning,
Traffic Control Warnings
Direct
V2V
Direct
V2V
Simple
RSU
19. 18
Intelligent Transportation Services Will Require Edge Computing
CAV Base Station Metro EPC Core Network Cloud Server
~115ms round trip, Bulk of latency is in metro/core networks.
5ms
12ms 20ms1ms
10ms 10ms
10ms 10ms
20ms9ms
5ms
1ms
2ms
5ms
12ms1ms
9ms
5ms
1ms
2ms ~35ms round trip
5G targeting 1ms end to end latency
CAV Base Station with Edge Computing
22. 21
Distributed Artificial Intelligence
Cloud
• Big Data Fusion
• Training Engines
• Inference Engines
• Localized Data Aggregation,
Information Generation
• Inference Engines for data
analytics
End Nodes
Edge
• Sensors, Data
Generation
• Inference Engines
for audio/visual
recognition
23. 22
ETSI Multi-Access Edge Compute Architectural Framework
Mobileedge
systemlevel
Mobile edge system level management
UE
3rd
party
3GPP
network
DSRC
network
External
network
Mobile edge
host level
management
Mobileedge
hostlevel
Networks
Mobile
edge app
Mobile edge applications
Mobile
edge
platform
Mobile edge host
Virtualization
Infrastructure (e.g. NFVI)
Mobile
edge app
Mobile
edge app
Mobile
edge app
24. 23
NFV - From Data Center to Edge
• Future network will connect billions of
people, devices and things (IoT)
• Distributed model –
− Distributed data centers (DCs)
− Geographically dispersed micro DCs
− Distributed NFV infrastructure – VNFs
placed everywhere between central DCs
and edge devices
− Migration toward multiple hierarchical
controller domains
• Edge / Aggregation nodes provide path
to supporting edge devices
− Connect edges to things, including smart
vehicles
25. 24
Motivations for Virtualization & Types
• Efficiency: Consolidation onto fewer
processors for higher hardware
utilization
− Oversubscription tolerated
• Ease of management
− Create/destroy virtual instances as
needed
− Migrate running instance to different
system
• Flexibility
− Use different versions of Linux
− Run legacy software or OS on HV
• Sandboxing– allows untrusted
software to be added to a system
(e.g. operator applications)
Hardware
App
OS
Hardware
App
OS
Hardware
App
OS
App
OS
Virtualization Layer
Linux
®
OS
App
OS
App
Hardware
Hypervisor
Linux®
KVM
RTOS
App
App
Linux®
App
App
Hardware
QEMU
Linux®
Hardware
Ap
p
Ap
p
App
Ap
p
Ap
p
App
Ap
p
Ap
p
App
DockerDockerLXC
containersGuests
27. 26
NXP in Automotive
Radar V2X
Vision/Fusion eCockpit/Infotainment
• 77 GHz proven in production
• Programmable chips
to 4 GHz
• MIPI interface 4x 20 Mbps
• Industry’s first RFCMOS
Radar SoC in the making
• Unique WW RFCMOS Solution
• Best-in-class Security
• >1 Million Days Field-Tested
• First Global OEM Design Award
• Next Gen: System optimized
Single Antenna 1-chip
• Many Core
• ASIL B-D
• Many Core
• GPUs
• Radio
- AM/FM
- Digital (DAB, HD)
28. 27
NXP in Networking/Telco
Service Provider
Wireless & Wired Equipment
NXP Digital Networking Market NXP QorIQ processors offer server class
performance for real time control and high touch
data services in wireless and wireline infrastructure
Enterprise / Data Center
Network Infrastructure
General Embedded
Mil/Aero, Industrial, Printing & Imaging
Data/Cloud
Enterprise
Service
Provider
Multi-access
Edge
Industrial
29. 28
We Care About the “V” and the “I”
Vehicle Intelligent Intersection / ITS “Spot”
DSRC DSRC
Sense – Vision, RADAR, DSRC Rx, GPS
Think – Sensor fusion, motion planning
Act – Motion control
Communicate – DSRC Tx, Broadband
Infotainment
Sense – Vision, RADAR, DSRC Rx, Wide Area
sensors from Cloud
Think – Sensor fusion, flow optimization, anomaly
detection, motion planning
Act – Traffic light control, DSRC traffic control
message, Avoidance directives
Communicate – DSRC Tx, Broadband Infotainment
Hotspot, Analytics data to Cloud
30. 29
Intelligent Traffic Control/RSU Proof of Concept System
Cohda MK5
DSRC
Antenna
(vector)
Processing
PCIe
“Beige Box”
Sensor Processing
Automotive Multicore
SoC
PCIe
CPRI
5G Baseband
Processing
RF
XCVR
5G Radio Head
Gb Ethernet
Backhaul
Radar1
Radar2
Radar3
Radar4
Camera1
Camera2
Camera3
Camera4
L2 Switch
Gb Ethernet
10Gb
Ethernet
iHigh Perf Multicore SoC
VM1: Base Station
VM2: Traffic Sensing & Control
VM4
Control Processing
Datapath Packet Processing
Web cache, media server
Traffic Stats Reporting
VM3
Plotting & Tracking
Traffic Light Control
V2X Message Gen
33. 32
Summary
• Safety, security and infrastructure investments are prime drivers for assistive
technologies and intelligent transportation
• ITS infrastructure most cope with co-existence of autonomous vehicles and legacy
users for a protracted period of time
• ITS opens up expansion of existing driver assistance use cases
• ITS applications demand high levels of edge computing
• Successful delivery of ITS assistive use cases will demand inferencing capabilities
• ITS assistance will be a major consumer and driver of edge networking
• NXP Semiconductors has a long history of delivering reliable, secure solutions to
the automotive and networking markets
Quallity of life issue --- we don’t want to lay down more concrete; merge first two bullets (alternative is stacking, tunneling, etc.)
1,000 X in mobile data volume per geographical area
reaching a target ≥ 10 Tb/s/km2
1,000 X in number of connected devices reaching a
density ≥ 1M terminals/km2
100 X in user data rate reaching a peak terminal data
rate ≥ 10Gb/s
1/10 X in energy consumption compared to 2010
1/5 X in end-to-end latency
4
reaching 5 ms for e.g.
tactile Internet and radio link latency reaching a target ≤
1 ms for e.g. Vehicle to Vehicle communication
1/5 X in network management OPEX
1/1,000 X in service deployment time reaching a
complete deployment in ≤ 90 minutes
3
4
End-to-End latency should be understood as limited for
the case of terminals physically close, as nearby vehicles,
a swarm of robots in an automated factory, or a terminal
connecting to advanced services provided by a cloud
located within its backhaul.
8
You don’t have to use this one, just reinforced the idea that as a vehicle gets more autonomous, it will generate and receive more external data to help smooth its driving and optimize traffic flow.
Intelligent infrastructure works hand in hand with increasingly automated vehicles. Just as the cars drive more smoothly when they’re communicating with each other, they drive more smoothly when they’re communicating with the traffic lights and other roadside equipment.
The complementary nature of DSRC & 5G illustrated. The infrastructure is able to provide wireless hotspot capability, traffic control, and safety, with reduced wait times at intersections. Cars will be allowed through the intersection unless there is a conflict (including pedestrians).
In 5G with massive mimo antennas, there is a requirement for densification. Many smaller base stations, closer to the users. It is logical for the base stations to want to take advantage of power and connectivity provided for traffic control. We believe that traffic control infrastructure and cellular infrastructure will start to merge.
The latency advantage of edge computing…
Overall latency between server and UE: ~78 ms
UE application delivery latency: 1 ms
eNB<->UE latency: 5 ms
eNB latency: 12 ms
Transport network termination (NAPI/fastpath stack): 3 ms
Scheduling/queueing (femto scenario): 4 ms
L2/L1 (10% HARQ): 5 ms
CSP latency: 20 ms
Estimation based on publically available numbers
Internet latency: 20 ms
Estimation based on publically available numbers
Bulk of latency is in metro/core networks. Co-location of VNF in eNB removes this
We understand the complementary nature of 5G and DSRC because we play in both.