An overview of the application of Deep learning in the field of autonomous driving, especially in the application and deployment process of BMW. The document was written by Alexander Frickenstein on March 17, 2019, and presented relevant content about BMW's use of Deep learning in autonomous driving at the 2019 GTC Silicon Valley Conference
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Send your semester & Specialization name to our mail id :
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This paper deals with testing theory regarding glass cars and road conveyance for Ark Compositions from their initial mechanical theory through the eventual remote theory of roads and cars.
Project Chameleon developed Europe's first 3D printed electric vehicle in just a few months. The vehicle, called Chameleon, has a 3D printed plastic chassis and weighs just 150kg, making it very lightweight and energy efficient. It was created using Scaled Ltd's mass customization approach of combining standardized electric powertrain components with a flexible 3D printing manufacturing process to produce specialized electric vehicles at lower costs than traditional manufacturing methods.
Data Driven Development of Autonomous Driving at BMWDataWorks Summit
"The development of autonomous driving cars requires the handling of huge amounts of data produced by test vehicles and solving a number of critical challenges specific to the automotive industry.
In this talk we will describe these challenges and how we, at BMW, are overcoming them by adapting and reinventing existing big data solutions for our end-to-end data journey for autonomous driving. Our journey involves ingesting data produced by a variety of sensors into a dedicated Hadoop cluster, decoding the data, conducting quality control, processing and storing the data on the clusters, making it searchable, analyzing it and exposing it to the engineers working on the algorithms development.
In the first part of the talk we will present a general overview of the challenges we faced and the lessons we learned from them. In the second part we will deep dive into the most interesting technical issues. These include: dealing with automotive formats and standards that are not designed for distributed processing; defragmentation of sensory data; assuring the quality of the data coming from complex car hardware and software components; efficient data search across petabytes of data; and reprocessing the computing components running in the car inside the data center, which typically requires high performance computing."
Speakers:
Felix Reuthlinger, Data Engineer for Autonomous Driving, BMW Group
Dogukan Sonmez, Senior Software Engineer, BMW Group
Examining BMW´s Open Architecture for Telematic Applications - H Michelmfrancis
This document discusses BMW's examination of an open architecture for telematic applications in vehicles. It outlines some of the challenges of increasing vehicle electronics complexity and proposes an open platform approach using standardization. Examples of BMW research applications are provided, including a navigation system that allows seamless routing between vehicle and mobile devices, and a personal information assistant for synchronizing contact and calendar data across devices. Standardization efforts like OSGi are seen as important to enable an open component-based architecture in vehicles.
Let's integrate CAD/BIM/GIS on the same platform: A practical approach in rea...SANGHEE SHIN
I gave this keynote talk at 3D GeoInfo conference held at Singapore on 25th September 2019. I shared my experiences in integrating CAD/BIM/GIS on the web platform and introduced mago3D from the technical point of view.
SOLVE THE CHALLENGE OF DATA HARVESTING WITH A SMART RECORDING TOOLCHAINiQHub
The document discusses challenges with collecting and processing large amounts of data from autonomous vehicle testing. It proposes a smart data harvesting approach using in-vehicle and edge processing to generate metadata, filter out unnecessary data, and perform initial analysis before uploading information to data centers. This shifts more of the data processing workload earlier in the pipeline to reduce costs associated with storage, transport and late-stage analysis of raw sensor data. The approach aims to keep data integrity and synchronization, monitor the recording and transport process, and provide early visibility into the most valuable data content.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
This paper deals with testing theory regarding glass cars and road conveyance for Ark Compositions from their initial mechanical theory through the eventual remote theory of roads and cars.
Project Chameleon developed Europe's first 3D printed electric vehicle in just a few months. The vehicle, called Chameleon, has a 3D printed plastic chassis and weighs just 150kg, making it very lightweight and energy efficient. It was created using Scaled Ltd's mass customization approach of combining standardized electric powertrain components with a flexible 3D printing manufacturing process to produce specialized electric vehicles at lower costs than traditional manufacturing methods.
Data Driven Development of Autonomous Driving at BMWDataWorks Summit
"The development of autonomous driving cars requires the handling of huge amounts of data produced by test vehicles and solving a number of critical challenges specific to the automotive industry.
In this talk we will describe these challenges and how we, at BMW, are overcoming them by adapting and reinventing existing big data solutions for our end-to-end data journey for autonomous driving. Our journey involves ingesting data produced by a variety of sensors into a dedicated Hadoop cluster, decoding the data, conducting quality control, processing and storing the data on the clusters, making it searchable, analyzing it and exposing it to the engineers working on the algorithms development.
In the first part of the talk we will present a general overview of the challenges we faced and the lessons we learned from them. In the second part we will deep dive into the most interesting technical issues. These include: dealing with automotive formats and standards that are not designed for distributed processing; defragmentation of sensory data; assuring the quality of the data coming from complex car hardware and software components; efficient data search across petabytes of data; and reprocessing the computing components running in the car inside the data center, which typically requires high performance computing."
Speakers:
Felix Reuthlinger, Data Engineer for Autonomous Driving, BMW Group
Dogukan Sonmez, Senior Software Engineer, BMW Group
Examining BMW´s Open Architecture for Telematic Applications - H Michelmfrancis
This document discusses BMW's examination of an open architecture for telematic applications in vehicles. It outlines some of the challenges of increasing vehicle electronics complexity and proposes an open platform approach using standardization. Examples of BMW research applications are provided, including a navigation system that allows seamless routing between vehicle and mobile devices, and a personal information assistant for synchronizing contact and calendar data across devices. Standardization efforts like OSGi are seen as important to enable an open component-based architecture in vehicles.
Let's integrate CAD/BIM/GIS on the same platform: A practical approach in rea...SANGHEE SHIN
I gave this keynote talk at 3D GeoInfo conference held at Singapore on 25th September 2019. I shared my experiences in integrating CAD/BIM/GIS on the web platform and introduced mago3D from the technical point of view.
SOLVE THE CHALLENGE OF DATA HARVESTING WITH A SMART RECORDING TOOLCHAINiQHub
The document discusses challenges with collecting and processing large amounts of data from autonomous vehicle testing. It proposes a smart data harvesting approach using in-vehicle and edge processing to generate metadata, filter out unnecessary data, and perform initial analysis before uploading information to data centers. This shifts more of the data processing workload earlier in the pipeline to reduce costs associated with storage, transport and late-stage analysis of raw sensor data. The approach aims to keep data integrity and synchronization, monitor the recording and transport process, and provide early visibility into the most valuable data content.
Solutions for ADAS and AI data engineering using OpenPOWER/POWER systemsGanesan Narayanasamy
The ultimate goal of ADAS feature development is to make our roads safer and better suited for fully autonomous vehicles in the long run. Still, manufacturers and buyers shouldn’t underestimate the importance of ADAS for meeting current automotive challenges. The most significant impact of advanced driver assistance systems is in providing drivers with essential information and automating difficult and repetitive tasks. This increases safety for everyone on the road
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-julian
For more information about embedded vision, please visit:
http://www.embedded-vision.com
David Julian, CTO and Founder of Netradyne, presents the "Addressing Corner Cases in Embedded Computer Vision Applications" tutorial at the May 2019 Embedded Vision Summit.
Many embedded vision applications require solutions that are robust in the face of very diverse real-world inputs. For example, in automotive applications, vision-based safety systems may encounter unusual configurations of road signs, or unfamiliar temporary barriers around construction sites. In this talk, Julian presents the approach that his company uses to address these “corner cases” in the development of Netradyne’s intelligent driver-safety monitoring system (IDMS).
The essence of the company' approach is establishing a virtuous cycle which begins with running analytics at the edge and identifying scenarios of interest and corner cases on the embedded edge device. Data from these cases is then uploaded to the cloud, where it is labeled and then utilized for training new deep learning and analytics models. These new models are then deployed to the embedded device to enable improved performance and begin the cycle anew. Juian also looks at how his company uses this virtuous cycle to develop and deploy new features. Additionally, he shows how his company leverages customer expertise to help identify corner cases at scale.
Business proposal of an adaptable & simple connected vehicle platform. Pitkraft can be adopted with current production vehicles. In future, Pitkraft can be a platform for connected vehicle & service aggregation.
opening-remarks-From Vehicle Centric to People Centric.pdfxmumiao
The document discusses BMW hosting the 11th Ethernet & IP Automotive Technology conference in 2021. It will be a hybrid event with both in-person and online attendees. BMW pioneered the use of Ethernet in vehicles in 2008. The keynote will allow BMW to welcome participants and showcase its leadership in automotive Ethernet. The keynote may discuss topics like future vehicle electronics architectures, the successful launch of the SP21 model, and challenges from changing mobility trends.
Big Data has emerged as a powerful new technology paradigm. To manage the massive data generated by social media, online transactions, Weblogs, or sensors, Big Data incorporates innovative technologies in data management (unstructured, semi-structured and structured), processing, real-time analytics, and visualization. It is also useful for reporting in circumstances where a relational database approach is not effective or too costly. This Big Data project is to be primarily exposed to utilize the tools. Tool usage, programming, algorithms and application development are covered in relevant courses.
Dirk Wisselmann of BMW Group presented BMW's roadmap for automated driving. BMW is developing technologies like traffic jam assistants and remote controlled parking that increase automation and integration of functions. By 2020, BMW aims to offer highly automated driving functions through continued scaling of technologies and field tests in cooperation with partners like Continental. However, standardized testing and legal frameworks are still needed for widespread deployment of highly automated vehicles.
This document summarizes an IBM presentation on emerging cloud migration approaches including AI planning, chatbots, and beyond. The presentation discusses using AI and machine learning to improve various phases of the cloud migration process, such as workload classification, selection, and planning. It also covers automating migration tasks and using conversational interfaces like chatbots to assist throughout the lifecycle. The document provides examples of how these approaches have helped customers successfully migrate to the cloud.
BMW is transforming its business to focus on electric vehicles and sustainability. It plans to offer electric models across all segments by 2023 and increase electric vehicle deliveries to 50% by 2025. BMW is heavily investing in research and development of new EV technologies and charging infrastructure to position itself for long-term success. It faces challenges from competitors like Tesla that have changed customer expectations through digital services and software-focused electric vehicles. BMW is adapting its strategy and business model to this new competitive landscape through increased electric vehicle production, partnerships, and enhancing its digital offerings and customer experience.
This document discusses the challenges of developing automated driving technologies in a rapidly changing business environment. It notes that innovation is occurring extremely quickly in areas like artificial intelligence and computing, but regulation is still being developed without precedence. This can lead to conflicts, like how to develop high-performance algorithms for tasks like pedestrian motion prediction while complying with privacy regulations like GDPR. The document also compares model-based and data-driven engineering approaches used for automated driving systems and discusses how organizational structure impacts product architecture.
I gave this talk at the ITEA3 Smart City Business Event. In this talk, I introduced mago3D project, an open source based digital twin platform first. And then shared the real cases that I and my company have carried out in various industries. I concluded the talk with what I've learnt from these real projects. Some food for thoughts around digital twin were provided for further discussion.
This document discusses digitalization of systems engineering and its benefits. It provides examples of integrating electric power systems for naval ships and the challenges of engineering hybrid and electric vehicle systems. Model-based systems engineering is presented as an approach to address challenges through a platform-based framework connecting multidisciplinary stakeholders. The document concludes with a case study of BMW's development of the X5 hybrid vehicle system using modeling and simulation within a platform-based process.
The automobile industry is facing demands on all fronts, including the need to optimize manufacturing and streamline supply chains and logistics and the demand for newer, higher-performing cars. 3D printing Bangalore is one technique that is assisting in meeting these difficulties.
3D printing Bangalore is being investigated in more and more fields of vehicle manufacturing. Aside from fast prototyping, the technology is often used to manufacture tooling and, in some cases, end components.
With the number of automotive 3D print online applications, here are some of the most compelling examples of automakers using the technology to improve production:
https://makenica.com/
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/aimotive/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-kishonti
For more information about embedded vision, please visit:
http://www.embedded-vision.com
László Kishonti, CEO of AImotive, presents the "Shifts in the Automated Driving Industry" tutorial at the May 2019 Embedded Vision Summit.
2018 will have a lasting effect on the self-driving industry, as key stakeholders have turned from the unattainable goal of full autonomy by 2021 to more realistic development and productization roadmaps. This will in turn result in consolidation in the automated driving industry, which is currently too fragmented. To realize self- driving and gain regulatory, consumer and (in the case of startups) investor trust, Kishonti suggests in this presentation, the walled-garden approaches of the industry must change to a more collaborative approach.
To support collaboration, cooperation and standardization, companies must adapt the way they create their software solutions and move to more modular designs. The companies with the most effective and meaningful collaborations will be the ones that survive the inevitable consolidation of the automated driving industry.
State of mago3D, An Open Source Based Digital Twin PlatformSANGHEE SHIN
I gave this talk at the FOSS4G Thailand 2019 which was held at Chulalongkorn University, Bangkok on 4th Nov 2019. I talked about the recent achievements and improvements of mago3D project, an open source based Digital Twin platform. mago3D(http mago3d.com) is relatively new project first released in July 2017. The ultimate goal of mago3D is developing an open source based digital twin platform that can replicate and simulate the real world objects, processes, and phenomena on web environment. mago3D has been used in various industry sectors including ship building, urban management, indoor data management, and national defense. In this talk I showcased several real projects that used the mago3D and shared what I learnt from these projects. Also I introduced new features and future plan of mago3D.
Cost-Effective Single-Camera Multi-Car Parking Monitoring and Vacancy Detecti...toukaigi
This document describes a system for monitoring parking spaces using a single camera. It uses a combination of static image analysis techniques like edge detection and histogram classification on individual frames, as well as dynamic analysis across frames using blob tracking. The static analysis provides estimates of occupancy for each space, while dynamic analysis updates spaces where cars are moving in or out. The system was tested on a parking lot with 56 spaces, achieving over 90% accuracy in reporting empty spots in real-time. It provides a low-cost solution for monitoring large parking areas with a single camera.
Civil Simulate is a powerful 3D interactive simulation and visualization tool. Using literally a single mouse click, Civil Simulate is able to turn any AutoCAD® Civil 3D® or CivilCAD projects into hyper realistic, fully rendered drive-through 3D simulation.
Civil Simulates incorporates a built-in 3D modeler which independently renders the CAD drawing’s RAW data. Therefore, the resulting 3D simulation offers high accuracy of ±0.8 In to the CAD drawing and is able to accurately display defined elements such as cross section signs, junctions, intersections, roundabouts, bridges, flyovers, etc.
Civil Simulate offers smooth correlation between the CAD drawing and the 3D simulation. Every change made in the design is instantly viewable in the simulation. The need for additional software to bridge the gaps between the CAD and the 3D simulation is eliminated.
Offering an extremely easy to operate and intuitive user interface, Civil Simulate turns a costly simulation which takes weeks to create, into an immediate and affordable part of any scale design project.
The simplicity of having realistic 3D simulation at hand, even while still in preliminary design stages, can support and assist both design and decision making processes. From road appearance and geometry to safety audits and line-of-sight visibility checks all can be performed from within the simulation to be later addressed in the CAD design.
Civil Simulate can easily place an Orthophoto or an aerial photography directly onto the 3D simulation to further enhance simulation’s reality and authenticity.
Civil Simulate interactive capabilities allow fully rendered drive through simulation from the driver’s point of view.
The document proposes a commuter pooling and campus vehicle sharing system using GM Spark EVs. It would involve setting up charging poles and parking spaces around the main facilities of a campus area covering around 3 square km. Benefits include reduced parking and infrastructure costs for employers, lower transportation costs for employees, reduced traffic and environmental impacts for the local municipality, and a real-world system for GM to test its electric vehicles and transportation solutions. A map shows proposed locations for charging poles and parking spaces around buildings including apartment complexes, a school, commercial area, and campus facilities.
James Goel, MIPI Technical Steering Group chair, shares a state-of-the-art MASS (MIPI Automotive SerDes Solutions) display architecture that leverages the latest MIPI DSI-2℠ protocols using VDC-M visually lossless compression algorithms to optimize pixel bandwidth within tightly constrained display systems.
The BMW Vision ConnectedDrive concept vehicle will make its world debut at the 2011 Geneva Motor Show, demonstrating BMW's leadership in developing innovative driver assistance systems and mobility services. The two-seater roadster concept utilizes innovative connectivity functionality to optimize comfort, safety, and the infotainment experience for both the driver and passenger. New technologies and design concepts enhance the driving pleasure typically provided by BMW vehicles.
EV Charging at MFH Properties by Whitaker JamiesonForth
Whitaker Jamieson, Senior Specialist at Forth, gave this presentation at the Forth Addressing The Challenges of Charging at Multi-Family Housing webinar on June 11, 2024.
Solutions for ADAS and AI data engineering using OpenPOWER/POWER systemsGanesan Narayanasamy
The ultimate goal of ADAS feature development is to make our roads safer and better suited for fully autonomous vehicles in the long run. Still, manufacturers and buyers shouldn’t underestimate the importance of ADAS for meeting current automotive challenges. The most significant impact of advanced driver assistance systems is in providing drivers with essential information and automating difficult and repetitive tasks. This increases safety for everyone on the road
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-julian
For more information about embedded vision, please visit:
http://www.embedded-vision.com
David Julian, CTO and Founder of Netradyne, presents the "Addressing Corner Cases in Embedded Computer Vision Applications" tutorial at the May 2019 Embedded Vision Summit.
Many embedded vision applications require solutions that are robust in the face of very diverse real-world inputs. For example, in automotive applications, vision-based safety systems may encounter unusual configurations of road signs, or unfamiliar temporary barriers around construction sites. In this talk, Julian presents the approach that his company uses to address these “corner cases” in the development of Netradyne’s intelligent driver-safety monitoring system (IDMS).
The essence of the company' approach is establishing a virtuous cycle which begins with running analytics at the edge and identifying scenarios of interest and corner cases on the embedded edge device. Data from these cases is then uploaded to the cloud, where it is labeled and then utilized for training new deep learning and analytics models. These new models are then deployed to the embedded device to enable improved performance and begin the cycle anew. Juian also looks at how his company uses this virtuous cycle to develop and deploy new features. Additionally, he shows how his company leverages customer expertise to help identify corner cases at scale.
Business proposal of an adaptable & simple connected vehicle platform. Pitkraft can be adopted with current production vehicles. In future, Pitkraft can be a platform for connected vehicle & service aggregation.
opening-remarks-From Vehicle Centric to People Centric.pdfxmumiao
The document discusses BMW hosting the 11th Ethernet & IP Automotive Technology conference in 2021. It will be a hybrid event with both in-person and online attendees. BMW pioneered the use of Ethernet in vehicles in 2008. The keynote will allow BMW to welcome participants and showcase its leadership in automotive Ethernet. The keynote may discuss topics like future vehicle electronics architectures, the successful launch of the SP21 model, and challenges from changing mobility trends.
Big Data has emerged as a powerful new technology paradigm. To manage the massive data generated by social media, online transactions, Weblogs, or sensors, Big Data incorporates innovative technologies in data management (unstructured, semi-structured and structured), processing, real-time analytics, and visualization. It is also useful for reporting in circumstances where a relational database approach is not effective or too costly. This Big Data project is to be primarily exposed to utilize the tools. Tool usage, programming, algorithms and application development are covered in relevant courses.
Dirk Wisselmann of BMW Group presented BMW's roadmap for automated driving. BMW is developing technologies like traffic jam assistants and remote controlled parking that increase automation and integration of functions. By 2020, BMW aims to offer highly automated driving functions through continued scaling of technologies and field tests in cooperation with partners like Continental. However, standardized testing and legal frameworks are still needed for widespread deployment of highly automated vehicles.
This document summarizes an IBM presentation on emerging cloud migration approaches including AI planning, chatbots, and beyond. The presentation discusses using AI and machine learning to improve various phases of the cloud migration process, such as workload classification, selection, and planning. It also covers automating migration tasks and using conversational interfaces like chatbots to assist throughout the lifecycle. The document provides examples of how these approaches have helped customers successfully migrate to the cloud.
BMW is transforming its business to focus on electric vehicles and sustainability. It plans to offer electric models across all segments by 2023 and increase electric vehicle deliveries to 50% by 2025. BMW is heavily investing in research and development of new EV technologies and charging infrastructure to position itself for long-term success. It faces challenges from competitors like Tesla that have changed customer expectations through digital services and software-focused electric vehicles. BMW is adapting its strategy and business model to this new competitive landscape through increased electric vehicle production, partnerships, and enhancing its digital offerings and customer experience.
This document discusses the challenges of developing automated driving technologies in a rapidly changing business environment. It notes that innovation is occurring extremely quickly in areas like artificial intelligence and computing, but regulation is still being developed without precedence. This can lead to conflicts, like how to develop high-performance algorithms for tasks like pedestrian motion prediction while complying with privacy regulations like GDPR. The document also compares model-based and data-driven engineering approaches used for automated driving systems and discusses how organizational structure impacts product architecture.
I gave this talk at the ITEA3 Smart City Business Event. In this talk, I introduced mago3D project, an open source based digital twin platform first. And then shared the real cases that I and my company have carried out in various industries. I concluded the talk with what I've learnt from these real projects. Some food for thoughts around digital twin were provided for further discussion.
This document discusses digitalization of systems engineering and its benefits. It provides examples of integrating electric power systems for naval ships and the challenges of engineering hybrid and electric vehicle systems. Model-based systems engineering is presented as an approach to address challenges through a platform-based framework connecting multidisciplinary stakeholders. The document concludes with a case study of BMW's development of the X5 hybrid vehicle system using modeling and simulation within a platform-based process.
The automobile industry is facing demands on all fronts, including the need to optimize manufacturing and streamline supply chains and logistics and the demand for newer, higher-performing cars. 3D printing Bangalore is one technique that is assisting in meeting these difficulties.
3D printing Bangalore is being investigated in more and more fields of vehicle manufacturing. Aside from fast prototyping, the technology is often used to manufacture tooling and, in some cases, end components.
With the number of automotive 3D print online applications, here are some of the most compelling examples of automakers using the technology to improve production:
https://makenica.com/
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/aimotive/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-kishonti
For more information about embedded vision, please visit:
http://www.embedded-vision.com
László Kishonti, CEO of AImotive, presents the "Shifts in the Automated Driving Industry" tutorial at the May 2019 Embedded Vision Summit.
2018 will have a lasting effect on the self-driving industry, as key stakeholders have turned from the unattainable goal of full autonomy by 2021 to more realistic development and productization roadmaps. This will in turn result in consolidation in the automated driving industry, which is currently too fragmented. To realize self- driving and gain regulatory, consumer and (in the case of startups) investor trust, Kishonti suggests in this presentation, the walled-garden approaches of the industry must change to a more collaborative approach.
To support collaboration, cooperation and standardization, companies must adapt the way they create their software solutions and move to more modular designs. The companies with the most effective and meaningful collaborations will be the ones that survive the inevitable consolidation of the automated driving industry.
State of mago3D, An Open Source Based Digital Twin PlatformSANGHEE SHIN
I gave this talk at the FOSS4G Thailand 2019 which was held at Chulalongkorn University, Bangkok on 4th Nov 2019. I talked about the recent achievements and improvements of mago3D project, an open source based Digital Twin platform. mago3D(http mago3d.com) is relatively new project first released in July 2017. The ultimate goal of mago3D is developing an open source based digital twin platform that can replicate and simulate the real world objects, processes, and phenomena on web environment. mago3D has been used in various industry sectors including ship building, urban management, indoor data management, and national defense. In this talk I showcased several real projects that used the mago3D and shared what I learnt from these projects. Also I introduced new features and future plan of mago3D.
Cost-Effective Single-Camera Multi-Car Parking Monitoring and Vacancy Detecti...toukaigi
This document describes a system for monitoring parking spaces using a single camera. It uses a combination of static image analysis techniques like edge detection and histogram classification on individual frames, as well as dynamic analysis across frames using blob tracking. The static analysis provides estimates of occupancy for each space, while dynamic analysis updates spaces where cars are moving in or out. The system was tested on a parking lot with 56 spaces, achieving over 90% accuracy in reporting empty spots in real-time. It provides a low-cost solution for monitoring large parking areas with a single camera.
Civil Simulate is a powerful 3D interactive simulation and visualization tool. Using literally a single mouse click, Civil Simulate is able to turn any AutoCAD® Civil 3D® or CivilCAD projects into hyper realistic, fully rendered drive-through 3D simulation.
Civil Simulates incorporates a built-in 3D modeler which independently renders the CAD drawing’s RAW data. Therefore, the resulting 3D simulation offers high accuracy of ±0.8 In to the CAD drawing and is able to accurately display defined elements such as cross section signs, junctions, intersections, roundabouts, bridges, flyovers, etc.
Civil Simulate offers smooth correlation between the CAD drawing and the 3D simulation. Every change made in the design is instantly viewable in the simulation. The need for additional software to bridge the gaps between the CAD and the 3D simulation is eliminated.
Offering an extremely easy to operate and intuitive user interface, Civil Simulate turns a costly simulation which takes weeks to create, into an immediate and affordable part of any scale design project.
The simplicity of having realistic 3D simulation at hand, even while still in preliminary design stages, can support and assist both design and decision making processes. From road appearance and geometry to safety audits and line-of-sight visibility checks all can be performed from within the simulation to be later addressed in the CAD design.
Civil Simulate can easily place an Orthophoto or an aerial photography directly onto the 3D simulation to further enhance simulation’s reality and authenticity.
Civil Simulate interactive capabilities allow fully rendered drive through simulation from the driver’s point of view.
The document proposes a commuter pooling and campus vehicle sharing system using GM Spark EVs. It would involve setting up charging poles and parking spaces around the main facilities of a campus area covering around 3 square km. Benefits include reduced parking and infrastructure costs for employers, lower transportation costs for employees, reduced traffic and environmental impacts for the local municipality, and a real-world system for GM to test its electric vehicles and transportation solutions. A map shows proposed locations for charging poles and parking spaces around buildings including apartment complexes, a school, commercial area, and campus facilities.
James Goel, MIPI Technical Steering Group chair, shares a state-of-the-art MASS (MIPI Automotive SerDes Solutions) display architecture that leverages the latest MIPI DSI-2℠ protocols using VDC-M visually lossless compression algorithms to optimize pixel bandwidth within tightly constrained display systems.
The BMW Vision ConnectedDrive concept vehicle will make its world debut at the 2011 Geneva Motor Show, demonstrating BMW's leadership in developing innovative driver assistance systems and mobility services. The two-seater roadster concept utilizes innovative connectivity functionality to optimize comfort, safety, and the infotainment experience for both the driver and passenger. New technologies and design concepts enhance the driving pleasure typically provided by BMW vehicles.
Similar to BMW:Application and Deployment of Deep Learning in Autonomous Driving.pdf (20)
EV Charging at MFH Properties by Whitaker JamiesonForth
Whitaker Jamieson, Senior Specialist at Forth, gave this presentation at the Forth Addressing The Challenges of Charging at Multi-Family Housing webinar on June 11, 2024.
Understanding Catalytic Converter Theft:
What is a Catalytic Converter?: Learn about the function of catalytic converters in vehicles and why they are targeted by thieves.
Why are They Stolen?: Discover the valuable metals inside catalytic converters (such as platinum, palladium, and rhodium) that make them attractive to criminals.
Steps to Prevent Catalytic Converter Theft:
Parking Strategies: Tips on where and how to park your vehicle to reduce the risk of theft, such as parking in well-lit areas or secure garages.
Protective Devices: Overview of various anti-theft devices available, including catalytic converter locks, shields, and alarms.
Etching and Marking: The benefits of etching your vehicle’s VIN on the catalytic converter or using a catalytic converter marking kit to make it traceable and less appealing to thieves.
Surveillance and Monitoring: Recommendations for using security cameras and motion-sensor lights to deter thieves.
Statistics and Insights:
Theft Rates by Borough: Analysis of data to determine which borough in NYC experiences the highest rate of catalytic converter thefts.
Recent Trends: Current trends and patterns in catalytic converter thefts to help you stay aware of emerging hotspots and tactics used by thieves.
Benefits of This Presentation:
Awareness: Increase your awareness about catalytic converter theft and its impact on vehicle owners.
Practical Tips: Gain actionable insights and tips to effectively prevent catalytic converter theft.
Local Insights: Understand the specific risks in different NYC boroughs, helping you take targeted preventive measures.
This presentation aims to equip you with the knowledge and tools needed to protect your vehicle from catalytic converter theft, ensuring you are prepared and proactive in safeguarding your property.
Expanding Access to Affordable At-Home EV Charging by Vanessa WarheitForth
Vanessa Warheit, Co-Founder of EV Charging for All, gave this presentation at the Forth Addressing The Challenges of Charging at Multi-Family Housing webinar on June 11, 2024.
Charging and Fueling Infrastructure Grant: Round 2 by Brandt HertensteinForth
Brandt Hertenstein, Program Manager of the Electrification Coalition gave this presentation at the Forth and Electrification Coalition CFI Grant Program - Overview and Technical Assistance webinar on June 12, 2024.
Charging Fueling & Infrastructure (CFI) Program by Kevin MillerForth
Kevin Miller, Senior Advisor, Business Models of the Joint Office of Energy and Transportation gave this presentation at the Forth and Electrification Coalition CFI Grant Program - Overview and Technical Assistance webinar on June 12, 2024.
Dahua provides a comprehensive guide on how to install their security camera systems. Learn about the different types of cameras and system components, as well as the installation process.
Implementing ELDs or Electronic Logging Devices is slowly but surely becoming the norm in fleet management. Why? Well, integrating ELDs and associated connected vehicle solutions like fleet tracking devices lets businesses and their in-house fleet managers reap several benefits. Check out the post below to learn more.
Charging Fueling & Infrastructure (CFI) Program Resources by Cat PleinForth
Cat Plein, Development & Communications Director of Forth, gave this presentation at the Forth and Electrification Coalition CFI Grant Program - Overview and Technical Assistance webinar on June 12, 2024.
BMW:Application and Deployment of Deep Learning in Autonomous Driving.pdf
1. DEEP LEARNING IN THE FIELD OF AUTONOMOUS
DRIVING
AN OUTLINE OF THE DEPLOYMENT PROCESS FOR ADAS AND AD
Alexander Frickenstein, 3/17/2019
2. Page 2
GTC 2019 - Silicon Valley| Deep Learning for Autonomous Driving at BMW | 03/20/19
AUTONOMOUS DRIVING AT BMW
3. Page 3
GTC 2019 - Silicon Valley| Deep Learning for Autonomous Driving at BMW | 03/20/19
AUTONOMOUS DRIVING AT BMW
▪ BMW Autonomous Driving Campus in Unterschleißheim (Munich), established in 2017
▪ 1400 Employees incl. Partners (Sensor-processing, Data-Analytics, ML, Driving-Strategy, HW-Architecture)
▪ 81 Feature teams (incl. Partners), working in 2 weekly sprints (LESS)
▪ 30 PhDs
‘Raw data are good data’ -Unknown Author-
▪ BMW AD research fleet consist of 85 cars collecting 2TB/h per car
→ High resolution sensor data, like LIDAR, Camera
►Insight into three PhD-projects, which are driven by the AD strategy at BMW
4. Page 4
GTC 2019 - Silicon Valley| Deep Learning for Autonomous Driving at BMW | 03/20/19
CONTENT
▪ Introduction: Design Process of ML-Applications for AD
▪ Exemplary projects; which are driven by the AD strategy at BMW
1. Fine-Grained Vehicle Representations for AD
2. Self-Supervised Learning of the Drivable Area of AD
3. CNN Optimization Techniques for AD
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DESIGN PROCESS OF ML-APPLICATIONS FOR AD
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DESIGN PROCESS OF ML-APPLICATIONS FOR AD
▪ An rough outline of the deployment*
process for ADAS and AD
▪ Inspired by Gajski-Kuhn chart (or Y diagram) [1]
▪ Design of real-world applications include:
- Multiple domains (structural, modelling, optimization)
- Abstraction levels
- Knowledge sharing is essential for the drive of inovation
(e.g. Car manufactures, technology companies)
*Presented projects gives an academic insight of PhD-candidates
*Datasets shown here are not used for commercial purpose
Fig. 1: Design Process of AD-Applications.
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FINE-GRAINED VEHICLE REPRESENTATIONS FOR AD
BY THOMAS BAROWSKI, MAGDALENA SZCZOT AND SEBASTIAN HOUBEN
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GTC 2019 - Silicon Valley| Deep Learning for Autonomous Driving at BMW | 03/20/19
FINE-GRAINED VEHICLE REPRESENTATIONS FOR AD
BY THOMAS BAROWSKI, MAGDALENA SZCZOT AND SEBASTIAN HOUBEN
▪ Motivation: a detailed understanding of complex traffic scenes:
- State and possible intentions of other traffic participants
- Precise estimation of a vehicle pose and category
- Be aware of dynamic parts, e.g. Doors, Trunks
- React fast and appropriate to safety critical situations
Thomas Barowski, Magdalena Szczot and Sebastian
Houben: Fine-Grained Vehicle Representations for
Autonomous Driving, ITSC, 2018,
10.1109/ITSC.2018.8569930.
Fig. 2: Exemplary 2D visualization of fragmentation
levels in the Cityscapes[3] segmentation benchmark.
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FINE-GRAINED VEHICLE REPRESENTATIONS FOR AD
▪ Goal:
- Learn new vehicle representations by semantic segmentation
- Three vehicle fragmentation levels (Course → Fine → Full):
- Dividing vehicle into part areas, based on materials and function
- Embedding pose information
- Annotating representations on CAD-Models
- Empirically examined on VKITTY[4], Cityscapes[3]
▪ Core idea is to extend an existing image-dataset by manual labeling
▪ Data generation pipeline is an adaption of the
semi-automated method from Chabot et al. [5]
▪ Instead, annotation is done on a set of models (3D Car Models)
Fig. 2: Exemplary 2D visualization of fragmentation
levels in the Cityscapes[3] segmentation benchmark.
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SEMI-AUTOMATED LABELING PIPELINE (1)
▪ Vehicle Fragmentation Levels from ShapeNet (3D Model
Repository):
- Different car models including WorldNet synsets (>4000)
- Three fragmentation levels (Coarse (4) – Fine (9) – Full (27))
- Including classes for: Body, windows, lights, wheels, doors,
roof, side, trunk, wheels, windshiels
- In finer grained representations: model needs to solve
challenging task of separation between parts that share visual
cues but vary in position, e.g. individual doors
- Identify parts with small local visual context: representation
becomes suitable for pose estimation with high occlusion or
truncation
Fig. 3: Visualization of the annotated CAD models [5] .
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SEMI-AUTOMATED LABELING PIPELINE (2)
1. Apply well overlapping 3D bounding boxes to raw images
2. Suited model is selected based on the vehicle type or
dimensions of the model (L1-distance)
3. Mesh of the 3D-model is resize to fit the bounding box
and aligned to 3D space
4. Mesh is projected on the image plane:
→ Resulting in a segmentation map containing
fragmentation level information of the vehicle
5. Only pixels labeled as vehicle in the respective dataset are
propagated to the image
→ To overcome projection errors
→ Results in fine-grained dense representations
Fig. 4: Semi-automated labeling pipeline.
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FCN MODEL EXPLORATION
▪ Reimplemented FCN8 [6] and VGG16 [7] as backbone
▪ End to end training, using cross entropy loss
▪ Trained on 4-27 classes (based on fragmentation level)
▪ Plus classes of datasets
▪ Multi-GPU training (Kubernets and Horovod on DGX1)
→ Full fragmentation level → High resolution input images
▪ Aim: not loosing significant accuracy in non vehicle-related background classes
Fig. 5: FCN8 [6] with VGG16[7] backbone.
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FCN MODEL EXPLORATION
Experiment IoUclass IoUnon-parts IoUparts
VKITTY Baseline 68.77 68.77 -
VKITTY ShapeNet [15] Coarse 61.05 66.49 63.64
Fine 56.93 66.31 44.73
Full 36.67 58.22 27.44
Cityscapes ShapeNet [15] Coarse 49.56 48.81 52.96
Fine 48.63 50.88 44.20
Full 33.50 50.78 21.98
Tab. 1: Segmentation results for the three fragmentation levels, performed on VKITTY and Cityscapes using FCN8.
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FCN MODEL EXPLORATION – VKITTY AND CITYSCAPES
Fig. 6a: Qualitative results on VKITTY dataset for the three fragmentation levels.
Coarse: Fine: Full:
Fig. 6b: Qualitative results on Cityscapes dataset for the three fragmentation levels.
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02
SELF-SUPERVISED LEARNING OF THE DRIVABLE AREA OF AD
BY JAKOB MAYR, CHRISTIAN UNGER, FEDERICO TOMBARI
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SELF-SUPERVISED LEARNING OF THE DRIVABLE AREA OF AD
▪ Motivation:
- Automated approach for generating training data for the task of drivable
area segmentation → Training Data Generator (TDG)
- Acquisition of large scale datasets with associated ground-truth still poses
an expensive and labor-intense problem
▪ Deterministic stereo-based approach for ground-plane detection:
Fig. 7a: Automated generated data of TDG. Fig. 7b: Segmentation of DNN trained on TDG.
Jakob Mayr, Christian Unger, Federico Tombari:
Self-Supervised Learning of the Drivable Area
for Autonomous Driving, iROS, 2018.
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WHY GROUND-PLANE DETECTION?
▪ Important aspect is the planning of safe and comfortable driving maneuvers
▪ Knowledge about the environment of the vehicle
▪ especially drivable areas (important role in ADAS and AD)
▪ e.g. road ahead/ drivable area is blocked by obstacles
▪ Parallel processing of GPUs allow frame based semantic segmentation
▪ Why Automated Data-Labeling?
- Pace and cost pressure
- Labeling is expensive
- Existing datasets do not suit the desired application:
o Technical aspects: e.g. field of view, mounting position, camera geometry
o Environmental conditions: e.g. weather condition, time, street types
Technical Aspect of Cityscapes:
images show part of the hood,
initialization of the ground-plane
model including non-ground plane
disparity is necessary!
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AUTOMATED LABELING PIPELINE
▪ Based on so-called v-disparity map [8]:
- Different use cases
- No fine tuning of existing models required
Fig. 8: Automated labeling pipeline.
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CNN-MODEL EXPLORATION (1)
▪ Automatically generated data are used to train Unet and SegNet → low resolution inputs (512*256 and 480*360)
▪ Models are trained only on automatically generated datasets
▪ Evaluation is performed by using human labeled ground-truth data, e.g. Cityscape [3], Kitty [2]
→ Drivable (road, parking) and non-drivable area (side walks, pedestrians)
▪ Observations:
- Low detection in lateral direction
- Noisy data of TDG → generate robust CNN model
- Dynamic objects are detected reliably
Fig. 9a: SegNet segmentation. Fig. 9b: U-Net segmentation.
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CNN-MODEL EXPLORATION (1)
▪ Robustness of model - Flipping images upside down:
- SegNet [9] → Not capable of detecting ground-plane
- UNet [10] → Detects ground-plane
Fig. 9c: Flipped SegNet segmentation. Fig. 9d: Flipped U-Net segmentation.
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CNN-MODEL EXPLORATION (2)
▪ Performance:
- Data generation:
- Hand labeling Cityscape [3] 19d
- Using automated labeling 3.5h (CPU) not parallelized on GPU yet
- U-Net [10] : 10 fps on Titan X
- ResNet [9].:4.4fps on Titan X
→ Optimization of DNNs comes into account
→ Out of the box CNNs come along with substantial drawbacks
Cityscapes KITTY
Approach Rec Prec Acc IoU Rec Prec Acc IoU
Training Data Generator (TDG) 70.84 91.49 85.35 66.46 81.87 58.07 86.58 51.45
Unet trained on auto. TDG labels 85.29 92.83 91.27 80.01 87.25 81.35 94.31 72.70
SegNet trained on auto. TDG labels 85.75 76.10 85.29 67.56 90.96 70.01 91.35 65.45
Tab. 2: Segmentation results for the TDG, performed on Cityscapes [3] and Kitty [2] using U-Net [10] and SegNet [9].
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03
CNN OPTIMIZATION TECHNIQUES FOR AD
BY ALEXANDER FRICKENSTEIN, MANOJ VEMPARALA, WALTER STECHELE
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CNN OPTIMIZATION TECHNIQUES FOR AD
Fig. 10: How deployment of DNNs can be seen differently.
▪ Running example: Quantization of CNNs:
- Normally, floating-point PEs is 10× less energy
efficient compared to fixed point math.
- The step-size between two numbers could be
dynamic using floating-point numbers. This is
useful feature for different kinds of layers in CNN.
- Closing gap between CNN compute demand and
HW-accelerator is important
→ Trend to specialized HW-accelerator, e.g. Tesla
T4
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CNN OPTIMIZATION TECHNIQUES FOR AD
Fig. 11: Optimization design process.
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RESOURCE AWARE MULTICRITERIAL OPTIMIZATION
▪ Motivation:
- Out of the box DNNs require high performance HW-accelerator:
- YOLO [11] or SSD300 [12] require an Nvidia Titan X to run in real-time
→ Showing the high compute demand of those models
- No, SqueezeNet [13] is really not out of the box!
→ An 18,000 GPU super-computer is used for the model exploration
- Deploy DNNs on memory, performance and power-consumption
constraint embedded hardware is commonly time consuming
Fig. 11: Filter-wise pruning of convolutional layer.
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WHY RESOURCE-AWARE MULTICRITERIAL OPTIMIZATION?
▪ Optimization techniques are going hand in hand
(Filter-wise pruning and Quantization)
▪ CNN optimization depends on system,
algorithmic and system level in the design process
▪ DNNS need to be highly compressed to fit the HW for AD
→ Automotive rated memory is expensive
▪ Good data locality is essential for low-power applications
→ Extreme temperatures in cars (Siberia → Death Valley)
→ Active cooling obligatory?
▪ Fast deployment time is a crucial aspect for
agile SW deployment
→ Proposing a Pruning and Quantization scheme for CNNs
Fig. 12: Pruning and quantization for efficient embedded Applications of DN
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METHOD -PRUNING
▪ Fast magnitude based [14] pruning scheme → removing unimportant filter
▪ Based on a half-interval search (log2(n))
→ Explore optimal layer-wise pruning rate
▪ Varying pruning order to generate an optimized model either with respect to the
memory demand or execution time
▪ Process of removing weight-filter of a layer:
1. Identify Cost (L1-distance) of all weight-filter of a layer
2. Based on the half-interval search remove filter, which cost is below a threshold
3. Threshold is identified by half-interval search
4. Retrain model (SGD with momentum) with small learning rate
→ Momentum should be available before pruning
5. As accuracy of the CNN is maintained increase the pruning rate (half-interval
search)
6. Pruning is always applied to the layer which fits the desired optimization goal best
Fig. 13: Binary-search applied to prune CNN.
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METHOD - QUANTIZATION
▪ Quantization leads to a hardware friendly implementation
▪ Reducing the footprint of HW-components
▪ Lowering the memory bandwidth
▪ Improving the performance
→ Floating-point PE is 10x less efficient
compared to fixed-point unit
▪ Weight and activations are brought into the fixed-point
format with the notation <S,IB,FB>
- S: Sign bit
- IB: Integer bit
- FB: Fractional bit
▪ Stochastic rounding is used for approximation
Fig. 14: Pruning and quantization applied to CNN.
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MEMORY AND PERFORMANCE BENCHMARK
Fig. 15: Pruning rate and runtime of Deep Compression [14] and our approach.
Fig. 16: Runtime of VGG16 [7] on different HW-Accelerator.
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MEMORY AND PERFORMANCE BENCHMARK
Tab. 3: Performance and memory benchmark of our method applied to
VGG16.
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REFERENCES
[1] Grout Ian: Digital Systems Design with FPGAs and CPLDs, 2008.
[2] Andreas Geiger, Philip Lenz, Raquel Urtasun, et al.: Are we ready for autonomous driving? The KITTI vision benchmark suite, CVPR, 2012.
[3] Marius Cordts, Mohamed Omran, Sebastian Ramos, et al.: The Cityscapes Dataset for Semantic Urban Scene Understanding, CVPR, 2016.
[4] Adrien Gaidon, Qiao Wang, Yohann Cabon, et al.: Virtual Worlds as Proxy for Multi-Object Tracking Analysis, CVPR, 2016.
[5] Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, et al.: ShapeNet: An Information-Rich 3D Model Repository, 2015.
[6] Jonathan Long, Evan Shelhamer, Trevor Darrell: Fully convolutional networks for semantic segmentation, CVPR, 2015.
[7] Karen Simonyan, Andrew Zisserman: Very Deep Convolutional Networks for Large-Scale Image Recognition, ICLR, 2014.
[8] Raphael Labayrade,Didier Aubert,Jean-Philippe Tarel: Real time obstacle detection in stereovision on non flat road geometry through "v-
disparity" representation, IVS, 2002.
[9] Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla: SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,
IEEE Transactions on Pattern Analysis and Machine Learning, 2017.
[10] Olaf Ronneberger, Philipp Fischer, Thomas Brox: U-Net: Convolutional Networks for Biomedical Image Segmentation, Lecture Notes in
Computer Science, 2015.
[11] Joseph Redmon, Santosh Divvala, Ross Girshick: YOLO: Real-Time Object Detection, CVPR, 2014.
[12] Wei Liu, Dragomir Anguelov, Dumitru Erhan: SSD: Single Shot MultiBox Detector, ECCV, 2016.
[13] Forrest N. Iandola, Song Han, Matthew W. Moskewicz, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,
ICLR, 2017.
[14] Song Han, Jeff Pool, John Tran, William J. Dally: Learning both Weights and Connections for Efficient Neural Networks, CVPR, 2015.
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THANK YOU FOR THE ATTENTION