Automotus

automotus

Hi, I'm Valerio, software engineer from Italy. As product owner I learned on my skin how an application issue isn't just a technical stuff. For software-driven companies performance bottlenecks and bugs could create a negative impact on the users experience and can affect their perception about our product and work. Users don't spend their time to report bugs, they just stop using our application, moving to another one that fits their needs better. I'm one of those that publish new code changes almost every day and unfortunately it's quite impossible to anticipate all the problems that could happen after every release. In most of the projects I've worked on the 50% of the drawbacks for users were caused by simple code mistakes, often in the code executed in background (artisan commands or Jobs) where it's even more tricky to know if all is working fine or something is broken. Inspector is a composer package to add real-time monitoring in Laravel applications, it's very easy to install and use, and it takes just two minutes to get started. Thanks to Inspector you'll no longer need to spend a lot of time to monitor your application behaviour by logs manually, because an autonomous tool does this job for you 24/7, making it rise to the surface anything that could create problems for users. Let me show you how it works. Install the composer package Install our package by running the command below in your terminal: composer require inspector-apm/inspector-laravel Configure the API key Get a fresh API key by signing up for Inspector ( ) and creating a new application, it takes just a few seconds. https://app.inspector.dev/register You'll see installation instructions directly in the app screen: Put the API key into your environment file: INSPECTOR_API_KEY = 13 c 37 c 434 XXXXXXXXXXXX Test everything is working: Execute our command to check if your app send data to inspector correctly: test php artisan inspector: test Go to to explore your demo data. https://app.inspector.dev/home By default Inspector monitors: Database interactions Queued Jobs Artisan commands Email sent Notifications Unhandled Exceptions We just turned on the light in the 50% of our app executed in background. The next step is to monitor all transactions generated by user interactions. Incoming Web Requests monitoring To activate monitoring also when your app is executed due to an incoming http request you can use the WebRequestMonitoring middleware. It works like any other Laravel middleware you are familiar to so you are free to decide which routes need to be monitored based on your routes configuration or on your personal monitoring preferences. The most easy way to cover all your application routes is attaching the middleware in the class. App\Http\Kernel ...,
        \Inspector\Laravel\Middleware\WebRequestMonitoring::class,
    ], ...,
        \Inspector\Laravel\Middleware\WebRequestMonitoring::class,
    ]
] <?php /** * The application's route middleware groups. * * @var array */ protected $middlewareGroups = [ 'web' => [ 'api' => [ Instantly you will see transactions streams in your project’s dashboard: and for each transaction you can monitor what your application is executing in real-time: Enrich your timeline Inspector monitors database query, background jobs, artisan commands by default, but could be many critical statements in your code that need to be monitored in terms of performance and error: Http calls to external services (Data sync, Payment, etc) Function that deals with files (pdf, excel, images) Algorithms Thanks to our package you can add custom segments in your timeline in addition to those detected by default, to measure the impact that an hidden code block has on a transaction’s performance. Let me show you a real life example. Suppose you have a queued job that executes some database checks and an http request to an external service in background. Job and queries are reported automatically, but it could be interesting to monitor and measure the execution of the http request. Simply use the inspector helper function: ]); }
} <?php class TagUserAsActive extends Job { /** * @param User $user */ protected $user ; /** * Monitoring & Measure an external http request */ public function handle ( ) { // Start monitoring inspector()->addSegment( function ( ) { $this ->guzzle->post( '[mail-marketing-url]/add_tag' , [ 'email' => $htis ->user->email, 'tag' => 'active' , }, 'http' ); You will see the impact of the new segment in your timeline: Exceptions Alerting By default, every unhandled exception fired in your Laravel app will be reported automatically to be sure you’re alerted for unpredictable errors in real time. I wish that every change I make to my code could be perfect. But the reality is, this is not always the case. Some errors appear immediately after an update, while others pop up unpredictably. However, Inspector automates the detection of unknown issues so I no longer need to manually check the status of my apps continuously or wait reports directly from users. If something goes wrong I’ll receive a notification in real time, and after each release I can stay informed about the impact of the latest code refactor. If your code fires an exception but you don't want to block the execution, you can report the error to inspector manually for private monitoring about availability of the external system. } <?php try { // Your dangerous http call here... } catch (\ Exception $exception ) { // Report an exception intentionally to collect diagnostics data Inspector::reportException( $exception ) Furthermore, if the http request fails, you will be alerted in real time in your inbox to take a look at the error as it appears. Real-Time email notification You even get access to detailed information gathered by Inspector in real time: Conclusion When a customer reports to you that something isn't working, it forces you to drop whatever you are doing and start trying to reproduce the scenario, then recapture and reanalyze the logs in your own toolset. Getting a true picture of what's happening can require hours or, based on my experience, even days. Inspector can make a huge difference in terms of efficiency and productivity. By delegating application monitoring to an automous tool, allows you to solve 90% of the problems in the half the time, before users even know about them. Previously published at https://inspector.dev/laravel-real-time-performance-monitoring-using-inspector-part-1/

Laravel Real-Time Monitoring Using Inspector

Driving Innovation: Patents and Projects Shaping the Automotive Industry

ড্রাইভিং ইনোভেশন: পেটেন্ট এবং প্রকল্পগুলি মোটরগাড়ি শিল্পকে আকার দেয়

Impulsando la innovación: patentes y proyectos que configuran la industria automotriz

Poticanje inovacija: patenti i projekti koji oblikuju automobilsku industriju

イノベーションの推進: 自動車産業を形成する特許とプロジェクト

ນະວັດຕະກໍາການຂັບລົດ: ສິດທິບັດ ແລະໂຄງການສ້າງອຸດສາຫະກໍາລົດຍົນ

Go Kganna Boitlhamelo: Dipatente le Diprotšeke tše di Bopago Intasteri ya Dikoloi

Продвижение инноваций: патенты и проекты, формирующие автомобильную промышленность

Kutyaira Innovation: Patents uye Mapurojekiti Kugadzira Iyo Indasitiri Yemotokari

ขับเคลื่อนการสร้างสรรค์นวัตกรรม: สิทธิบัตรและโครงการที่กำหนดรูปลักษณ์ของอุตสาหกรรมยานยนต์

Pagmamaneho ng Innovation: Mga Patent at Proyekto na Humuhubog sa Industriya ng Automotive

Innovatsiyalarni qo'zg'atuvchi: avtomobil sanoatini shakllantiruvchi patentlar va loyihalar

I-Driving Innovation: Amalungelo Obunikazi Nemiklamo Ebumba Imboni Yezimoto

RemotiveLabs, a leader in automotive software, is excited to launch RemotiveTopology, a powerful toolset designed to redefine vehicle software development. It enables engineers to continuously simulate, test, and validate vehicle architectures on standard Linux laptops and in central CI pipelines - bringing more speed and iteration into key processes in automotive engineering. Why This Matters In an industry obsessed with the concept of the Software-Defined Vehicle, few address the real challenges developers face. RemotiveLabs challenges legacy automotive tools by introducing modern software practices into automotive architectures. This launch isn’t just about technology—it’s a startup with an ambitious vision, backed by bold collaborations and pioneering users. “Our launch of RemotiveTopology is a crucial step in turning development around—working collaboratively in a virtualized environment where the vehicle becomes the ‘physical twin,’” said Per Sigurdson, CEO and Co-Founder of RemotiveLabs. Virtualized Automotive Development with RemotiveTopology RemotiveTopology allows developers to mock entire platforms collaboratively, starting virtually running in a soft environment. The same tests can be run on a developer laptop or any CI/CD pipeline of choice, and the modular setup enables a smoother transition from SIL (Software-in-the-Loop) to HIL (Hardware-in-the-Loop). With early integration testing of vehicle networks, engineers can identify and fix issues before all the hardware is in place. Volvo Cars has been leveraging RemotiveLabs’ toolchain since the start in 2020, integrating it into software prototyping, development, and testing processes. This lightweight, open platform is gaining recognition within the company for setting its developers up for success. “Virtualization of automotive software is becoming an integral part of our developer experience, providing tooling for easy, efficient, and reusable testing. RemotiveLabs tools reduce dependencies on hardware, allowing our teams to iterate faster and more efficiently,” said Alwin Bakkenes, Head of Software Engineering at Volvo Cars. Beyond individual use cases, Aleksandar Filipov, CTO and Founder of RemotiveLabs, expressed how empowering it is for engineers to experience vehicle functionality without the need for physical interaction. “It is extremely encouraging to see how software engineers at our customers now can experience how a vehicle runs, even when they haven’t physically interacted with one before.” RemotiveTopology represents more than just a technological advancement—it’s a shift in how the automotive industry approaches software development. By embracing virtualization and modern software practices, companies like Volvo Cars are pushing the boundaries of innovation and efficiency, paving the way for a future where physical vehicles are the final step in a largely virtual development process. About RemotiveTopology RemotiveTopology by RemotiveLabs enables vehicle virtualization and integration testing directly from a developer’s laptop, using executable specifications to validate functionality early and accelerate collaboration. See the product page here and the Volvo Cars usecase here. About RemotiveLabs RemotiveLabs empowers automotive engineers by providing modern and flexible tooling for software development. The Malmö-based startup was founded in Sweden in 2020 and has been recognized as a game changer in software-defined vehicles with their open and developer-centric platform. Contact: Carin Lagerstedt, carin.lagerstedt@remotivelabs.com, +46706254464 RemotiveLabs, a leader in automotive software, is excited to launch RemotiveTopology, a powerful toolset designed to redefine vehicle software development. It enables engineers to continuously simulate, test, and validate vehicle architectures on standard Linux laptops and in central CI pipelines - bringing more speed and iteration into key processes in automotive engineering. Why This Matters In an industry obsessed with the concept of the Software-Defined Vehicle , few address the real challenges developers face. RemotiveLabs challenges legacy automotive tools by introducing modern software practices into automotive architectures. This launch isn’t just about technology—it’s a startup with an ambitious vision, backed by bold collaborations and pioneering users. Software-Defined Vehicle “Our launch of RemotiveTopology is a crucial step in turning development around—working collaboratively in a virtualized environment where the vehicle becomes the ‘physical twin,’” said Per Sigurdson, CEO and Co-Founder of RemotiveLabs. “Our launch of RemotiveTopology is a crucial step in turning development around—working collaboratively in a virtualized environment where the vehicle becomes the ‘physical twin,’” said Per Sigurdson, CEO and Co-Founder of RemotiveLabs. Virtualized Automotive Development with RemotiveTopology Virtualized Automotive Development with RemotiveTopology RemotiveTopology allows developers to mock entire platforms collaboratively, starting virtually running in a soft environment. The same tests can be run on a developer laptop or any CI/CD pipeline of choice, and the modular setup enables a smoother transition from SIL (Software-in-the-Loop) to HIL (Hardware-in-the-Loop). With early integration testing of vehicle networks, engineers can identify and fix issues before all the hardware is in place. Volvo Cars has been leveraging RemotiveLabs’ toolchain since the start in 2020, integrating it into software prototyping, development, and testing processes. This lightweight, open platform is gaining recognition within the company for setting its developers up for success. “Virtualization of automotive software is becoming an integral part of our developer experience, providing tooling for easy, efficient, and reusable testing. RemotiveLabs tools reduce dependencies on hardware, allowing our teams to iterate faster and more efficiently,” said Alwin Bakkenes, Head of Software Engineering at Volvo Cars. “Virtualization of automotive software is becoming an integral part of our developer experience, providing tooling for easy, efficient, and reusable testing. RemotiveLabs tools reduce dependencies on hardware, allowing our teams to iterate faster and more efficiently,” said Alwin Bakkenes, Head of Software Engineering at Volvo Cars. Beyond individual use cases, Aleksandar Filipov , CTO and Founder of RemotiveLabs, expressed how empowering it is for engineers to experience vehicle functionality without the need for physical interaction. Aleksandar Filipov “It is extremely encouraging to see how software engineers at our customers now can experience how a vehicle runs, even when they haven’t physically interacted with one before.” “It is extremely encouraging to see how software engineers at our customers now can experience how a vehicle runs, even when they haven’t physically interacted with one before.” RemotiveTopology represents more than just a technological advancement—it’s a shift in how the automotive industry approaches software development. By embracing virtualization and modern software practices, companies like Volvo Cars are pushing the boundaries of innovation and efficiency, paving the way for a future where physical vehicles are the final step in a largely virtual development process. About RemotiveTopology RemotiveTopology by RemotiveLabs enables vehicle virtualization and integration testing directly from a developer’s laptop, using executable specifications to validate functionality early and accelerate collaboration. See the product page here and the Volvo Cars usecase here . here here About RemotiveLabs RemotiveLabs empowers automotive engineers by providing modern and flexible tooling for software development. The Malmö-based startup was founded in Sweden in 2020 and has been recognized as a game changer in software-defined vehicles with their open and developer-centric platform. RemotiveLabs Contact: Carin Lagerstedt, carin.lagerstedt@remotivelabs.com , +46706254464 carin.lagerstedt@remotivelabs.com

RemotiveLabs Unveils RemotiveTopology: A New Tool for Virtualized Automotive Development

RemotiveLabs presenta RemotiveTopology: una nueva herramienta para el desarrollo automotriz virtualizado

RemotiveLabs Devwale RemotiveTopology: Yon Nouvo Zouti pou Virtualize Devlopman Otomobil

RemotiveLabs-ը ներկայացնում է RemotiveTopology. նոր գործիք՝ վիրտուալացված ավտոմոբիլային զարգացման համար

RemotiveLabs חושפת את RemotiveTopology: כלי חדש לפיתוח רכב וירטואלי

RemotiveLabs が RemotiveTopology を発表: 仮想化自動車開発のための新しいツール

RemotiveLabs RemotiveTopology ұсынады: Виртуалдандырылған автокөлікті дамытуға арналған жаңа құрал

RemotiveLabs បង្ហាញ RemotiveTopology៖ ឧបករណ៍ថ្មីសម្រាប់ការអភិវឌ្ឍន៍យានយន្តនិម្មិត

RemotiveLabs prezentuje RemotiveTopology: nowe narzędzie do wirtualizowanego rozwoju motoryzacyjnego

RemotiveLabs представляет RemotiveTopology: новый инструмент для разработки виртуализированных автомобилей

RemotiveLabs zbulon RemotiveTopology: Një mjet i ri për zhvillimin e virtualizuar të automobilave

РемотивеЛабс представља РемотивеТопологи: нови алат за виртуелизовани развој аутомобила

RemotiveLabs ra mắt RemotiveTopology: Một công cụ mới cho phát triển ô tô ảo hóa

Over the last few years, AI has revolutionized our lives by not just automating repetitive work but also seemingly developing the ability to "think" like a human being and tap into the creativity pool. Seriously, how many of you have used "Chat-GPT" to compose a poem or used "Suno" for another love song? Maybe it's time that we moved from a dark time when humans needed to be responsible for all the boring copy jobs to a more high-tech future where we are left to do the real killing, for example, negotiation and strategic planning. Data Scraping in The Era of AI Web scraping Tools with AI In modern times, we usually collect data online from one or multiple sources. The tedious process can be automated since the harvesting action is repetitive. To do this, there are many data collection tools available, or if you prefer, you can call them web scraping tools. Old ways of scraping data from the internet can be problematic because they rely on the HTML rule of the website to navigate to the target data. Once the HTML structure is changed, the scraping rule will be invalid. Not to mention modern websites tend to involve Javascript interaction to enhance user experience, which adds to the difficulty of getting the data precisely. With the help of AI, however, we can deal with website changes easily. Take one tool, for example. As a no-code-involved scraper tool, Octoparse is dedicated to integrating AI into its intuitive scraping interface. It leverages AI to improve the auto-detection of web page elements, making it easier for beginners to start scraping. The AI enhances the accuracy of identifying data fields, buttons, and other interactive elements on web pages, reducing the learning curve for new users. By simplifying the initial setup, users can quickly create effective scraping workflows without technical knowledge. For more advanced users, Octoparse's AI can assist in writing and adjusting scraping rules. Once trained, the AI can generate and modify the necessary code to accommodate changes in website structures. This capability ensures that scraping rules remain effective even as websites evolve, reducing the need for manual intervention and ongoing maintenance. Users can rely on the AI to handle complex adjustments, ensuring continuous data extraction with minimal disruptions. Robotic Process Automation (RPA) With AI There are also AI-based RPA tools (Robotic Process Automation) to automate any repetitive and regular steps within or between software and systems. “Robotic process automation is not a physical [or] mechanical robot,” says Chris Huff, chief strategy officer at Kofax. While it can mimic most human-computer interactions to carry out the most mundane and repetitive tasks and processes in the workplace, at high volume and speed. For example, imagine you need to move files from one place to another or conduct a freight booking. With AI joining automation, things can be done in a more intelligent way. For example, AI can help decide which document and file to be processed by using Natural Language Processing(NLP). AI can read and interpret the text and content, and categorize them for different automation workflows. We can also use simple natural language to talk with AI, so it can build the RPA workflow automatically for us according to our demands and even based on historical patterns and situations. The time when AI can be our powerful partner in life and at work has arrived! Data Analysis in The Era of AI Backed by the magic of machine learning, AI can process big and complex datasets and draw accurate predictions and insights by identifying patterns and anomalies. It's not just about crunching numbers. AI is getting way smarter than that nowadays. AI Data cleaning Since data is not always consistent in format and may include inaccuracy, AI can help with data cleaning and preprocessing by identifying anomalies such as duplicate entries, misspelled addresses, missing values, inconsistent formatting for locations, etc. Octoparse's AI also helps in the preliminary cleaning of extracted data. By applying AI algorithms to filter and refine the raw data, users can receive higher-quality outputs that are more useful for analysis. This automated cleaning process helps eliminate errors and inconsistencies, providing cleaner datasets that require less manual processing. As a result, users can focus on analyzing the data rather than spending time on tedious cleaning tasks. AI Data visualization AI can create interactive charts and graphs that reveal even the slightest change overlooked by the human eye. With real-time data constantly being fed to the AI system, the dashboard will reflect the latest trends and patterns for any prompt actions. For example, ThoughtSpot leverages AI and a search-driven interface to simplify data exploration and visualization. It connects to various data sources, consolidating information in one platform, and allows users to create logical data models that define relationships and context. By typing natural language queries into ThoughtSpot's search bar, users can have the AI interpret and fetch relevant data. The platform generates interactive charts, graphs, and dashboards based on these queries, which users can further customize. AI Data insights As humans, we excel at drawing insights from circumstances. But even the lowest level data analysts take a long time to master the skills of graph interpreting and data processing. Using AI, then, can greatly save us time and also the cost to get the insights we need. AI, with its powerful and fathomless NLP(Natural Language Processing) ability, can help us conduct predictive analysis as well as sentiment analysis. In E-commerce, AI-based data analysis solutions like Octoparse VOC help thousands of companies get a thorough understanding of how their products fare. From customer profile (who, when, where, why), positive/negative feedback to unmet needs and pre-purchase concerns, this tool(also including its extension) provides detailed information that can be crucial for later product development and marketing campaign direction. About price monitoring, some AI tools can make this process fast and easy. Competera is an AI-powered pricing platform designed to help retailers optimize pricing strategies. With algorithms and machine learning, it offers pricing optimization based on many factors like demand elasticity. It's clear that AI indeed plays a significant role in predictive analytics. By forecasting future patterns, AI-based data analyzing tools can help businesses stay ahead of the curve. Conclusion As AI learns and adapts, the era of data being a headache will be over. Humans, being the commanders, will be the ones to choose which possibility calculated by AI should be taken. Over the last few years, AI has revolutionized our lives by not just automating repetitive work but also seemingly developing the ability to "think" like a human being and tap into the creativity pool. Seriously, how many of you have used "Chat-GPT" to compose a poem or used "Suno" for another love song? Maybe it's time that we moved from a dark time when humans needed to be responsible for all the boring copy jobs to a more high-tech future where we are left to do the real killing, for example, negotiation and strategic planning. Data Scraping in The Era of AI Web scraping Tools with AI Web scraping Tools with AI In modern times, we usually collect data online from one or multiple sources. The tedious process can be automated since the harvesting action is repetitive. To do this, there are many data collection tools available, or if you prefer, you can call them web scraping tools . web scraping tools Old ways of scraping data from the internet can be problematic because they rely on the HTML rule of the website to navigate to the target data. Once the HTML structure is changed, the scraping rule will be invalid. Not to mention modern websites tend to involve Javascript interaction to enhance user experience, which adds to the difficulty of getting the data precisely. With the help of AI, however, we can deal with website changes easily. Take one tool, for example. As a no-code-involved scraper tool, Octoparse is dedicated to integrating AI into its intuitive scraping interface. It leverages AI to improve the auto-detection of web page elements, making it easier for beginners to start scraping. The AI enhances the accuracy of identifying data fields, buttons, and other interactive elements on web pages, reducing the learning curve for new users. By simplifying the initial setup, users can quickly create effective scraping workflows without technical knowledge. For more advanced users, Octoparse's AI can assist in writing and adjusting scraping rules. Once trained, the AI can generate and modify the necessary code to accommodate changes in website structures. This capability ensures that scraping rules remain effective even as websites evolve, reducing the need for manual intervention and ongoing maintenance. Users can rely on the AI to handle complex adjustments, ensuring continuous data extraction with minimal disruptions. Robotic Process Automation (RPA) With AI Robotic Process Automation (RPA) With AI There are also AI-based RPA tools (Robotic Process Automation) to automate any repetitive and regular steps within or between software and systems. AI-based RPA tools “Robotic process automation is not a physical [or] mechanical robot,” says Chris Huff, chief strategy officer at Kofax . While it can mimic most human-computer interactions to carry out the most mundane and repetitive tasks and processes in the workplace, at high volume and speed. For example, imagine you need to move files from one place to another or conduct a freight booking. Kofax With AI joining automation, things can be done in a more intelligent way. For example, AI can help decide which document and file to be processed by using Natural Language Processing(NLP). AI can read and interpret the text and content, and categorize them for different automation workflows. We can also use simple natural language to talk with AI, so it can build the RPA workflow automatically for us according to our demands and even based on historical patterns and situations. The time when AI can be our powerful partner in life and at work has arrived! Data Analysis in The Era of AI Backed by the magic of machine learning, AI can process big and complex datasets and draw accurate predictions and insights by identifying patterns and anomalies. It's not just about crunching numbers. AI is getting way smarter than that nowadays. AI Data cleaning AI Data cleaning Since data is not always consistent in format and may include inaccuracy, AI can help with data cleaning and preprocessing by identifying anomalies such as duplicate entries, misspelled addresses, missing values, inconsistent formatting for locations, etc. Octoparse 's AI also helps in the preliminary cleaning of extracted data. By applying AI algorithms to filter and refine the raw data, users can receive higher-quality outputs that are more useful for analysis. This automated cleaning process helps eliminate errors and inconsistencies, providing cleaner datasets that require less manual processing. As a result, users can focus on analyzing the data rather than spending time on tedious cleaning tasks. Octoparse AI Data visualization AI Data visualization AI can create interactive charts and graphs that reveal even the slightest change overlooked by the human eye. With real-time data constantly being fed to the AI system, the dashboard will reflect the latest trends and patterns for any prompt actions. For example, ThoughtSpot leverages AI and a search-driven interface to simplify data exploration and visualization. It connects to various data sources, consolidating information in one platform, and allows users to create logical data models that define relationships and context. By typing natural language queries into ThoughtSpot's search bar, users can have the AI interpret and fetch relevant data. The platform generates interactive charts, graphs, and dashboards based on these queries, which users can further customize. AI Data insights AI Data insights As humans, we excel at drawing insights from circumstances. But even the lowest level data analysts take a long time to master the skills of graph interpreting and data processing. Using AI, then, can greatly save us time and also the cost to get the insights we need. AI, with its powerful and fathomless NLP(Natural Language Processing) ability, can help us conduct predictive analysis as well as sentiment analysis. In E-commerce, AI-based data analysis solutions like Octoparse VOC help thousands of companies get a thorough understanding of how their products fare. From customer profile (who, when, where, why), positive/negative feedback to unmet needs and pre-purchase concerns, this tool(also including its extension) provides detailed information that can be crucial for later product development and marketing campaign direction. Octoparse VOC About price monitoring, some AI tools can make this process fast and easy. Competera is an AI-powered pricing platform designed to help retailers optimize pricing strategies. With algorithms and machine learning, it offers pricing optimization based on many factors like demand elasticity. It's clear that AI indeed plays a significant role in predictive analytics. By forecasting future patterns, AI-based data analyzing tools can help businesses stay ahead of the curve. Conclusion Conclusion As AI learns and adapts, the era of data being a headache will be over. Humans, being the commanders, will be the ones to choose which possibility calculated by AI should be taken.

How AI Automates Data Scraping and Data Analysis

কীভাবে এআই ডেটা স্ক্র্যাপিং এবং ডেটা বিশ্লেষণ স্বয়ংক্রিয় করে

Wie KI das Data Scraping und die Datenanalyse automatisiert

Cómo la IA automatiza la extracción y el análisis de datos

Comment l'IA automatise le grattage et l'analyse des données

AI डेटा स्क्रैपिंग और डेटा विश्लेषण को कैसे स्वचालित करता है

AIがデータスクレイピングとデータ分析を自動化する方法

AI가 데이터 스크래핑 및 데이터 분석을 자동화하는 방법

Como a IA automatiza a extração e análise de dados

Как ИИ автоматизирует сбор и анализ данных

Yapay Zeka, Veri Kazımayı ve Veri Analizini Nasıl Otomatikleştirir?

Cách AI tự động thu thập dữ liệu và phân tích dữ liệu

人工智能如何实现数据抓取和数据分析的自动化

One of the more intriguing aspects of the future of Artificial Intelligence (AI) is seen in the automotive industry and the problems and solutions found therein. This technological revolution is impacting various aspects of the industry, from manufacturing and supply chain management to the driving experience itself. Autonomous vehicles, for instance, promise to enhance safety, reduce traffic congestion, and provide mobility solutions for those unable to drive. Features like adaptive cruise control, lane-keeping assistance, and self-parking are already becoming standard. Billionaire entrepreneur Elon Musk describes his Tesla products as robots on wheels as opposed to cars or vehicles. Given the popularity his company has experienced, Musk sees a positive future for autonomous cars and aims to continue advancing the technology and finding a way for Tesla vehicles to achieve complete autonomous capabilities. In fact, Musk is so invested in his project and so confident it will work that he recently threatened his board of directors at Tesla to “give me 25% of the company or I won’t build AI and robotic products at the company,” after making clear that the company is worthless without those. Like Musk, millions of people around the world would like to see more autonomous vehicles on the roads in the hope they will improve their driving experience and reduce accidents. With advanced sensors, real-time data processing, and machine learning algorithms, these vehicles will make more accurate and faster decisions than human drivers, leading to safer roads. AI is doing a lot more than this of course. It is also revolutionizing the manufacturing process through predictive maintenance, quality control, and automation, and is enhancing the in-car experience by providing personalized settings and services. AI is undoubtedly reshaping the automotive industry, offering numerous benefits in terms of safety, efficiency, and personalization. Hopefully, further advancements will see traffic management systems, public transportation, and infrastructure working together to create a seamless and efficient transportation network. But there is more that needs to be done before the technology can be used widely by millions of people in hundreds of cities around the world. AI systems must be capable of handling diverse and unpredictable road conditions, which, at the moment, they aren’t. Many electric vehicles have faced several crashes attributed to adverse weather conditions. These incidents often involve the vehicles' advanced driver-assistance systems (ADAS), like autopilot, which can struggle with detecting and reacting to road conditions such as heavy rain, snow, or ice. While driver judgment is among the most common causes of car accidents around the world, and while AI will almost certainly prevent crashes by using better judgment, the technology still must undergo improvements to deal with inclement weather and unsafe road conditions to be fully reliable. The deployment of AI in vehicles raises complex regulatory and legal questions, such as liability in case of accidents involving autonomous vehicles. To this end, governments and industry stakeholders are currently developing clear and comprehensive regulations to deal with this issue. The future of AI integrated into autonomous vehicles holds great promise, and so many of us are excited about what the future holds for the automotive industry. One of the more intriguing aspects of the future of Artificial Intelligence (AI) is seen in the automotive industry and the problems and solutions found therein. This technological revolution is impacting various aspects of the industry, from manufacturing and supply chain management to the driving experience itself. Autonomous vehicles, for instance, promise to enhance safety, reduce traffic congestion, and provide mobility solutions for those unable to drive. Features like adaptive cruise control, lane-keeping assistance, and self-parking are already becoming standard. Billionaire entrepreneur Elon Musk describes his Tesla products as robots on wheels as opposed to cars or vehicles. Given the popularity his company has experienced, Musk sees a positive future for autonomous cars and aims to continue advancing the technology and finding a way for Tesla vehicles to achieve complete autonomous capabilities. In fact, Musk is so invested in his project and so confident it will work that he recently threatened his board of directors at Tesla to “give me 25% of the company or I won’t build AI and robotic products at the company,” after making clear that the company is worthless without those. threatened Like Musk, millions of people around the world would like to see more autonomous vehicles on the roads in the hope they will improve their driving experience and reduce accidents. With advanced sensors, real-time data processing, and machine learning algorithms, these vehicles will make more accurate and faster decisions than human drivers, leading to safer roads. AI is doing a lot more than this of course. It is also revolutionizing the manufacturing process through predictive maintenance, quality control, and automation, and is enhancing the in-car experience by providing personalized settings and services. AI is undoubtedly reshaping the automotive industry, offering numerous benefits in terms of safety, efficiency, and personalization. Hopefully, further advancements will see traffic management systems, public transportation, and infrastructure working together to create a seamless and efficient transportation network. But there is more that needs to be done before the technology can be used widely by millions of people in hundreds of cities around the world. AI systems must be capable of handling diverse and unpredictable road conditions, which, at the moment, they aren’t. Many electric vehicles have faced several crashes attributed to adverse weather conditions. These incidents often involve the vehicles' advanced driver-assistance systems (ADAS), like autopilot, which can struggle with detecting and reacting to road conditions such as heavy rain, snow, or ice. several crashes While driver judgment is among the most common causes of car accidents around the world, and while AI will almost certainly prevent crashes by using better judgment, the technology still must undergo improvements to deal with inclement weather and unsafe road conditions to be fully reliable. most common causes The deployment of AI in vehicles raises complex regulatory and legal questions, such as liability in case of accidents involving autonomous vehicles. To this end, governments and industry stakeholders are currently developing clear and comprehensive regulations to deal with this issue. The future of AI integrated into autonomous vehicles holds great promise, and so many of us are excited about what the future holds for the automotive industry.

The Promising Future of AI and Automotive Technology

Get to know RemotiveBroker’s gRPC API and how it simplifies prototyping, building & testing automotive software. Build software the way you want and in the programming language of your choice. We make things easier with a Python wrapper and the gRPC stubs available in a public repo on our GitHub. Get stuff done with our gRPC API Want to make a quick prototype using Python? Interested in Rust? The RemotiveBroker gRPC API can be used to read and write vehicle signal data in your language of choice. This means that you can use the tools you prefer to quickly build applications that depend on real vehicle signal data. Given a simple configuration and a signal database file, you can get to work right away with recorded data or inside a physical vehicle with live data. Use the same API regardless if you are working with recorded or live data, write your application once and test it on your desktop and inside a vehicle! You can reuse previously recorded drive cycles, e.g. in a deterministic ECU testing setup or when prototyping graphical user interfaces. Once you are satisfied with your work you can, in a true shift-left spirit, bring it to a test rig or physical vehicle knowing that the code has been tested. Paired with our collaboration platform, RemotiveCloud, you can quickly scale up your development efforts with multiple virtualized RemotiveBrokers! Get started with three lines of code Start a RemotiveBroker with three lines of code: $ git clone https://github.com/remotivelabs/remotivebroker-bootstrap.git
$ cd remotivebroker-bootstrap
$ docker-compose up -d Try our free 3-minute demo or check out some of our stuff on Github, e.g the APIs, simple samples, ProtoPie integration) and go to the repo for running the bootstrap RemotiveBroker on your desktop. What is a RemotiveBroker? The RemotiveBroker is a flexible developer tool that fit into most setups. it exposes vehicle signal data via a pub/sub gRPC API. We support the most common vehicle networks like CAN, Flexray, LIN, and Automotive Ethernet – enabled by .dbx, .xml (fibex), .ldf and .arxml. We also proved E2E Protection. Make it even easier with API wrappers and gRPC stubs We have a public repo for our wrappers and stubs. Anyone is welcome to submit a pull request with a wrapper in their favorite language, get started with our gRPC proto files. Our ambition is to have wrappers and stubs/clients available for everyone. Advantages of gRPC Given a simple configuration, the RemotiveBroker will expose a gRPC API that puts you in control. With the broad language support that gRPC has you can use what you prefer, e.g Node.js, Ruby, C# or Kotlin. gRPC leverages HTTP/2 and a binary message format, this helps to reduce network bandwidth and decrease latency. This allows our RemotiveBroker to send large amounts of data in real-time, like in the case of high-speed CAN & CAN-FD or FlexRay. Python wrapper We have a Python wrapper API for a subset of the RemotiveBroker API to make it even easier to build applications. It is also available for easy installation through the Python Package Index. This wrapper API makes it easier to perform common operations like connecting to a RemotiveBroker and setting up vehicle signal data subscriptions. Connect to a RemotiveBroker and set up a signal subscription: # Connect to RemotiveBroker
client = Client()
client.on_connect = lambda c: print(f"Yay! - connected as {c.client_id} to {c.url}")
client.on_signals = lambda signals: print(signals.to_json())
client.connect(url="http://localhost:50051")

subscription = client.subscribe(
signal_names=["Vehicle.Speed"],
namespaces=["vss"],
changed_values_only=False) Typescript and Javascript stubs/clients Since we are working with gRPC-web in our web clients, we have both Typescript and Javascript stubs/clients available for grpc-web. It is also available via npm/yarn. While not as useful as a proper wrapper API, the stubs/clients are required when working with gRPC. However, just looking at the stubs is helpful when exploring the API. Subscribe to frame frequency distribution: // Setup request
const config = new FramesDistributionConfig()
const namespace = new NameSpace()namespace.setName(namespaceName)
config.setNamespace(namespace)

// Subscribe
this._frameDistributionStream =  
this._networkServiceClient.subscribeToFramesDistribution(config) Our tooling and APIs are constantly evolving. Follow us on GitHub and LinkedIn to not miss out on more helpful developer content. If you have wishes or thoughts for what we should develop next don’t be a stranger! Get to know RemotiveBroker’s gRPC API and how it simplifies prototyping, building & testing automotive software. Build software the way you want and in the programming language of your choice. We make things easier with a Python wrapper and the gRPC stubs available in a public repo on our GitHub. programming language Get stuff done with our gRPC API Want to make a quick prototype using Python? Interested in Rust? The RemotiveBroker gRPC API can be used to read and write vehicle signal data in your language of choice. This means that you can use the tools you prefer to quickly build applications that depend on real vehicle signal data. Given a simple configuration and a signal database file, you can get to work right away with recorded data or inside a physical vehicle with live data. RemotiveBroker Use the same API regardless if you are working with recorded or live data, write your application once and test it on your desktop and inside a vehicle! You can reuse previously recorded drive cycles, e.g. in a deterministic ECU testing setup or when prototyping graphical user interfaces. Once you are satisfied with your work you can, in a true shift-left spirit, bring it to a test rig or physical vehicle knowing that the code has been tested. Paired with our collaboration platform, RemotiveCloud , you can quickly scale up your development efforts with multiple virtualized RemotiveBrokers! RemotiveCloud Get started with three lines of code Get started with three lines of code Start a RemotiveBroker with three lines of code: $ git clone https://github.com/remotivelabs/remotivebroker-bootstrap.git
$ cd remotivebroker-bootstrap
$ docker-compose up -d $ git clone https://github.com/remotivelabs/remotivebroker-bootstrap.git
$ cd remotivebroker-bootstrap
$ docker-compose up -d Try our free 3-minute demo or check out some of our stuff on Github, e.g the APIs , simple samples , ProtoPie integration ) and go to the repo for running the bootstrap RemotiveBroker on your desktop . free 3-minute demo APIs simple samples ProtoPie integration bootstrap RemotiveBroker on your desktop What is a RemotiveBroker? What is a RemotiveBroker? The RemotiveBroker is a flexible developer tool that fit into most setups. it exposes vehicle signal data via a pub/sub gRPC API. We support the most common vehicle networks like CAN, Flexray, LIN, and Automotive Ethernet – enabled by .dbx, .xml (fibex), .ldf and .arxml. We also proved E2E Protection. Make it even easier with API wrappers and gRPC stubs Make it even easier with API wrappers and gRPC stubs We have a public repo for our wrappers and stubs. Anyone is welcome to submit a pull request with a wrapper in their favorite language, get started with our gRPC proto files. Our ambition is to have wrappers and stubs/clients available for everyone. public repo Advantages of gRPC Advantages of gRPC Given a simple configuration, the RemotiveBroker will expose a gRPC API that puts you in control. With the broad language support that gRPC has you can use what you prefer, e.g Node.js, Ruby, C# or Kotlin. gRPC leverages HTTP/2 and a binary message format, this helps to reduce network bandwidth and decrease latency. This allows our RemotiveBroker to send large amounts of data in real-time, like in the case of high-speed CAN & CAN-FD or FlexRay. Python wrapper Python wrapper We have a Python wrapper API for a subset of the RemotiveBroker API to make it even easier to build applications. It is also available for easy installation through the Python Package Index. This wrapper API makes it easier to perform common operations like connecting to a RemotiveBroker and setting up vehicle signal data subscriptions. RemotiveBroker Python Package Index. Connect to a RemotiveBroker and set up a signal subscription: Connect to a RemotiveBroker and set up a signal subscription: # Connect to RemotiveBroker
client = Client()
client.on_connect = lambda c: print(f"Yay! - connected as {c.client_id} to {c.url}")
client.on_signals = lambda signals: print(signals.to_json())
client.connect(url="http://localhost:50051")

subscription = client.subscribe(
signal_names=["Vehicle.Speed"],
namespaces=["vss"],
changed_values_only=False) # Connect to RemotiveBroker
client = Client()
client.on_connect = lambda c: print(f"Yay! - connected as {c.client_id} to {c.url}")
client.on_signals = lambda signals: print(signals.to_json())
client.connect(url="http://localhost:50051")

subscription = client.subscribe(
signal_names=["Vehicle.Speed"],
namespaces=["vss"],
changed_values_only=False) Typescript and Javascript stubs/clients Typescript and Javascript stubs/clients Since we are working with gRPC-web in our web clients, we have both Typescript and Javascript stubs/clients available for grpc-web. It is also available via npm/yarn . While not as useful as a proper wrapper API, the stubs/clients are required when working with gRPC. However, just looking at the stubs is helpful when exploring the API. npm/yarn Subscribe to frame frequency distribution: Subscribe to frame frequency distribution: // Setup request
const config = new FramesDistributionConfig()
const namespace = new NameSpace()namespace.setName(namespaceName)
config.setNamespace(namespace)

// Subscribe
this._frameDistributionStream =  
this._networkServiceClient.subscribeToFramesDistribution(config) // Setup request
const config = new FramesDistributionConfig()
const namespace = new NameSpace()namespace.setName(namespaceName)
config.setNamespace(namespace)

// Subscribe
this._frameDistributionStream =  
this._networkServiceClient.subscribeToFramesDistribution(config) Our tooling and APIs are constantly evolving. Follow us on GitHub and LinkedIn to not miss out on more helpful developer content. GitHub LinkedIn If you have wishes or thoughts for what we should develop next don’t be a stranger!

Revolutionize Automotive Software Development with RemotiveBroker's gRPC API

RemotiveBroker-এর gRPC API-এর সাহায্যে অটোমোটিভ সফটওয়্যার ডেভেলপমেন্টে বিপ্লব ঘটান

Revolutionieren Sie die Entwicklung von Automobilsoftware mit der gRPC-API von RemotiveBroker

Revolucione el desarrollo de software automotriz con la API gRPC de RemotiveBroker

Révolutionnez le développement de logiciels automobiles avec l'API gRPC de RemotiveBroker

रिमोटिवब्रोकर के gRPC API के साथ ऑटोमोटिव सॉफ्टवेयर विकास में क्रांतिकारी बदलाव लाएँ

RemotiveBroker の gRPC API で自動車ソフトウェア開発に革命を起こす

RemotiveBroker의 gRPC API로 자동차 소프트웨어 개발에 혁명을 일으키세요

Revolucione o desenvolvimento de software automotivo com a API gRPC da RemotiveBroker

Произведите революцию в разработке автомобильного программного обеспечения с помощью gRPC API RemotiveBroker

RemotiveBroker'ın gRPC API'si ile Otomotiv Yazılım Geliştirmede Devrim Yapın

Cách mạng hóa việc phát triển phần mềm ô tô với API gRPC của RemotiveBroker

利用 RemotiveBroker 的 gRPC API 彻底改变汽车软件开发

**JERUSALEM, Israel, March 28th, 2024/Cyberwire/--**In 2023 alone, more than 10 customers and partners signed commercial agreements with C2A Security, including a global, long term enterprise agreement with an European Commercial Vehicle Manufacturer. C2A Security’s DevSecOps Platform, ‘EVSec’, has been gaining widespread traction as the automotive industry rushes to meet cybersecurity regulations and industry standards, such as UN Regulation No. 155, ISO/SAE 21434, Chinese GB Standards, and others. 2024 is a pivotal year for cybersecurity regulations in the automotive industry, as UN Regulation No. 155 goes into full effect. C2A Security’s EVSec risk-driven product security platform allows developers to focus on creative features and manage software and operations at scale, in an automated manner. EVSec automates archaic manual processes and enables cross-functional sharing and collaboration between teams, customers, and supply chains while offering full digital twin capabilities. EVSec applies continuous feedback from product operations and vulnerabilities to improve development and design, as part of agile software development. In 2023, C2A Security added multiple OEMs and Tier 1s to its portfolio of customers, successful evaluations, and partnerships such as Daimler Truck AG, BMW Group, Marelli, NTT Data, Siemens, and Valeo, among others. The collaboration with C2A Security supports the customers on the applicable standards and best practices for regulatory compliance. EVSec maps and automates the relevant standards and regulations, like ISO/SAE 21434 and UN Regulation No. 155, to simplify compliance efforts, which are essential to business success, as companies forge new grounds for their vehicles, develop innovative EV-powered vehicles, and plan for the EV infrastructure that supports them. “We’re thrilled that EVSec has proven so popular and effective, as companies like Daimler Truck AG choose it as its product security platform. Dealing with current and emerging regulatory demands, software development at scale and overall product security operations can be a limitation on business continuity of a company if not automated,” noted Roy Fridman, CEO, C2A Security. “At the end of 2023, we witnessed the first case of a premium car maker that stopped the sale of their most popular model in the European Union because it failed to comply with the regulation. To stay competitive companies must utilize advanced product security automation in their development and operations and we are excited to support them in achieving this goal.” Images available . here About C2A Security C2A Security is the only risk-driven DevSecOps Platform vendor that addresses the specific needs of car makers, Tier 1 suppliers, and mobility companies. Founded in 2016, C2A Security’s customers and technology partners include top-tier global players including Daimler Truck AG, BMW Group, Siemens, Valeo, ThunderSoft, Marelli, NTT Data, and Evvo Labs, among others. C2A Security transforms cybersecurity from being a company-wide limitation to a business value multiplier through advanced security automation and compliance to shorten software release times and decrease costs. Our vision is to turn product security into a seamless, automated, and transparent process, reducing time to deployment and costs of managing automotive software products and resources. C2A Security was founded by NDS/Cisco veteran Michael Dick, with its global headquarters in Jerusalem, Israel. . www.c2a-sec.com Contact Estee Yaari C2A Security estee.yaari@gmail.com This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

EVSec Risk Management And Automation Platform Gains Traction In Automotive Industry 

Let's learn about via these 24 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Automotive /Learn Repo 1. Electric Vehicles: to Buy or Not to Buy Electric Vehicles are Popular, but Should YOU Get One? 2. Designing an HMI in Automotive: A Stress-Free and Intuitive Way In 2020, sophisticated-car infotainment system designs are no longer impressive. Smartphone integration, voice assistance, recognition of driver distraction, assisted personalized driving, connected in-car payments can be easily taken for granted. With the rapid growth of automotive technologies, OEMs are being challenged to create HMIs that offer usability and ensure safety while standing out from the competition. Still, there are general rules that can be helpful to those interested in designing an HMI for connected cars and who are approaching this topic for the first time. 3. Top 10 Software Development Companies Working with Automotive AI Make choosing an AI software development partner easier for yourself: check out this top 10 selection of industry-approved teams. 4. Electric Cars: What Does the Future Hold? Better Batteries and Wireless Charging Electric Cars: What Does the Future Hold? 5. Automotive Industry Revolution: How BMW Leverages AR & VR in Car Production The main indicators of car competitiveness are price, quality, technical specifications, design, operating costs, and safety. In order to increase the competitiveness of automobiles, it is necessary to improve product development methods, introduce new design technologies, use computer capabilities, and mathematical modeling in all aspects of experimental design work. But the central aspect is to follow the latest technology trends. Important tools, such as AR and VR, are already widely used in the automotive industry. 6. SpaceX Technology May Have The Potential To Revolutionise The Future of Cars As the years have gone by, entrepreneur Elon Musk has undoubtedly become a household name. His broader visions, and not to mention wealth, has helped him to carve out a legendary mark in outer space and the automotive industry alike. 7. Electric Vehicles: Driving Towards a Sustainable Future With Artificial Intelligence Electric vehicles are redefining the automotive industry, and with supportive government policies and Artificial Intelligence, the future looks promising. 8. Why Hasn't Tesla Entered the Indian Market? The internet has been buzzing since Elon Musk announced that Tesla is coming to India next year. This is big news and not just for the people who are waiting to get their hands on a luxury electric car like Tesla but also for the entire electric vehicles (EV) industry in the country. 9. In-car Payment Systems Unlock New Opportunities for M2M Economy The full potential of in-car payment systems will be revealed in the machine-to-machine economy and a cooperative mobility ecosystem 10. How to Increase the Market Cap of Your Car Company The route to success in the automotive industry is a meticulous and ever-evolving process. 11. How the Rapid Demise of Entry-Level Cars has Increased Average Prices Why are manufacturers pushing entry-level cars out of production? Read and find out the reason why average car prices are rising. 12. Sensor-based Control in Cobots: Its Opportunities and Challenges Introduction of the very basic formulation of the major sensor-servo problem, and then presenting its most common approaches like touch-based, 13. How to Create an Image Processing App in 72 hours In November 2018, Volvo ran a hackathon that gathered talents from all over the globe to generate innovative ideas focused on undercarriage inspection automation. Our team created the best solution from scratch after 72 hours of live coding! Here’s how we did it. 14. Talking Cars: The Role of Conversational AI in Shaping the Future of Automobiles While self-driving cars remain a futuristic concept, ChatGPT will soon accompany drivers during their commutes through stimulating conversations. 15. The Future of Automotive Tech: Top Trends in 2023 Here’s a look at five of the biggest technology trends that will shape the automotive market in 2023. 16. Architecture Design of a Smart, Scalable, AI-driven Robotic Production Factory The core concepts of the architecture and design of a scalable, smart, robotics production factory is outlined. 17. Hydrogen Fuel Cell Vehicles are Not a Solution to Climate Change How is it that fuel cell vehicles using hydrogen as a fuel are touted as climate-friendly vehicles? California even subsidizes the purchase of a hydrogen fuel cell vehicle. The advocates never reveal the source of the hydrogen would most likely be coming as a byproduct of fracking in pursuit of natural gas. 18. The Hack to Driving Down Car Expenses A look at inflation's impact on gas and car insurance prices. 19. Tesla Stock Wears A Double Halo (Illustration: Mike Bloom) 20. Finding the Right Development Tool and Platform for a Vehicle HMI The key challenges and crossroads for selecting a platform and UI Development tool for an Automotive HMI system. 21. General Motor's Claims To Commercialise the Most Innovative Battery Technology GM has made the much-overlooked claim that by mid-decade, it expects to commercialize the most exotic of the stretch futuristic batteries currently on electric vehicle drawing boards. 22. How AI from Driver Technologies Company Helps To Protect the Motorists Across the world, more than 1.3 million people die in car accidents, and over 50 million people are seriously injured every year. That’s nearly 4,000 people each day. Drivers in developing nations are most at risk. Only 54% of the world’s motor vehicles are in developing countries, but 90% of the world’s fatal car accidents occur in those countries. Even within the wealthiest countries vehicle-related injury and death are directly correlated to personal and neighborhood incomes. 23. How To Build An OBD2 Reader For Car Diagnostics Full guide for car scanner ELM327 OBD2 reader app development. Connection, reading diagnostic data, and decoding vehicle errors. 24. Motor Vehicle Hacking: How Cybercriminals Hack Your Car Any system in a smart vehicle connecting to the Internet, fleet management software, or an EV charging network is a potential entry point for hackers. Thank you for checking out the 24 most read stories about Automotive on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

24 Stories To Learn About Automotive

Much has been made of digital transformation within the automotive industry, but the influence of reality technology has flown under the radar a little. However, is already cropping up throughout the industry with a series of exciting use cases that could herald a new era of driver safety and manufacturing efficiency. augmented reality The augmented reality (AR) market is set to surpass a value of , representing a CAGR of 46.8%. With such rapid growth anticipated, it’s inevitable that the technology will make its presence felt throughout the automotive industry. $97.76 billion by 2028 But how could AR help to improve the driving experience and road safety? Let’s explore five key reasons why augmented reality could bring vast improvements to the world of motoring: Heads-Up Navigation The role of augmented reality in the automotive industry is nothing new, and we can look back to Mercedes-Benz’s implementation of the technology in 2020 as a watershed moment in the utility of AR as a driving aid. Rather than using dashboard displays, Mercedes-Benz opted for an within its GLE model. This utility of augmented reality enabled the display of navigational instructions and information to be displayed over objects in real-time through a live camera view within the car’s dashboard. AR navigation screen The result was a driving experience that’s been unparalleled within the world of motoring. Drivers were treated to turning instructions, highlighted street names, addresses, and live terrain tracking to help provide more comprehensive levels of guidance without forcing the driver to look away from the road ahead. As continues to mature, we’re likely to see these heads-up navigational displays become more refined, helping to deliver live feedback in an unintrusive manner to help inform the decisions of drivers. augmented reality technology Augmented Car Manuals Augmented reality car manuals can be a great innovation for owners who could benefit from more visual and in-depth guidance on how to conduct maintenance on their vehicles. For tasks like replacing an air filter, AR manuals can offer to pave the way for flawless repair work. step-by-step immersive guidance This can also help owners to better acquaint themselves with their car’s functions through visual tutorials and actionable guides. could also be used in repairing damage to a vehicle. For instance, if a big data mechanism detects a cracked windshield, it could visually alert the driver to the crack, highlight the risk it poses, and suggest windshield crack repair options for owners to act on. This mechanism AR Showrooms There’s nothing more frustrating than having a customer visit a showroom to find that the model they’ve been researching isn’t in stock. With more cars offering vast ranges of features, colors, and mechanisms, it can be hard for interested customers to fully get to grips with the model they would like. This is where augmented reality and digital showrooms can come into play. By integraging of the vehicle and its correct mod-cons, customers can interact with their chosen car and add or remove features on the fly. detailed 3D renderings By exploring their chosen car in full augmented reality, it can vastly improve the customer experience throughout the buying process and pave the way for far greater satisfaction levels with the end result. Better Blindspot Control Another major benefit in terms of driver performance and safety comes in the form of augmented parking assistance and blindspot sensors. During the parking process, augmented reality can help to when reversing, adding an extra layer of protection when parking. This can also be applied to the blind spots concealed by the pillars between windows in a vehicle. By offering an augmented view of the objects around the driver when reversing, it can be a great asset in ensuring that no collisions can take place. visualize objects Enhanced Prototype Design Augmented reality can also be a major time-saving tool in the development of for new concepts. operational prototypes While modelling is a major part of the process, both augmented and virtual reality designers and endineers can offer more flexibility in modifying early-stage concepts and overcoming perceived limitations along the way. Reality technology can help to add new ideas on to a concept in the exact way they would appear on a physical model, helping to offer more creative freedom at less of a cost. Furthermore, both AR and VR can deliver different means of bench testing, helping to better explore prototypes, their functionality, and durability in the face of different simulated conditions. Welcoming Augmented Transformation The has been ripe for digital transformation when it comes to both the manufacturing process and ensuring the safety of drivers, but augmented reality stands as an opportunity to improve efficiency in these fields while also saving money in cases relating to manufacturing. automotive industry While much has been made of the growth of artificial intelligence, it may well be augmented reality that proves to be the smartest new tool at the disposal of manufacturers and motorists.

Automotive Augmentation: How AR is Changing the Driving Experience Forever

Tesla is here to stay and keep fighting for the electric car revolution —Elon Musk. Nikola Tesla doesn’t own Tesla. The 19th-century inventor discovered the properties of rotating electromagnetic fields, and there was no better way to pay homage to the pioneer than by naming the company after him. Elon Musk is not the founder but a Co-founder. But does being a co-founder not make you the founder? Let’s roll back the years of Tesla’s beginning. Tesla’s Beginning Martin Eberhard and Marc Tarpenning met at Wyse Technology a few years before they found on July 1, 2003, at 845 Oak Grove Avenue in Menlo Park. Tesla Their inspiration came from General Motors' aborted car line EV1 which ran between 1996-1999, and AC propulsion all-electric cars. Ian Wright joined the same year. As they sought investment for their new company, they were recommended to Elon Musk, who readily invested $6.5 million of the total $7.5 million received in February 2004. Elon became the largest shareholder and chairman of the company. Harold Rosen took J.B. Straubel to Elon Musk to help pitch his all-electric plane. Musk didn’t show interest, so Straubel grabbed the opportunity to pitch how all-electric cars could work with lithium-ion battery technology. Musk got shocked by this discovery because he had always thought ultracapacitors were the only way to go electric. Musk encouraged Straubel to join . He joined in May 2004. Tesla These five became the company’s Co-founders. Musk spearheaded Tesla’s second round of investment and secured $13 million in 2005. And the third round of investment was in May 2006. He raked in $40 million by encouraging his friends, Google Co-founders Larry Page, and Sergey Brin, to invest. Jeff Skoll, former president of , was among its investors. eBay In 2006, the Roadster prototype was launched. The company’s first product that’s capable of incurring profits. Before now, investors money powered the company. Power, Money, And Survival A leadership tussle struck Tesla before Roadster’s launch. The board asked Eberhard to step down as CEO in 2007. He then took the title of President of Technology and remained on the advisory board but left in 2008. Tarpenning, unsatisfied with the situation, left a month after. Michael Marks became CEO temporarily in August 2007 after Eberhard stepped down, then Ze’ev Dvori took over permanently in December 2007. He’s recognized for the successful launch of Roadster, selling over 2,500 Roadsters at $109,000- $200,000. Musk became CEO in October 2008 and laid off 20% of its workers due to the financial crisis. The crisis ate so deep that Tesla was on the verge of bankruptcy till Daimler AG bought 10% of its stake for $50 million in May 2009, and the US Department of Energy gave a $465 million loan by June 2009. On June 29, 2010, because it needed as much capital as possible. Tesla entered the US Stock and raised $226 million, $17 per share, in its IPO The company survived this financial crisis. The Lightyears In 2012, Roadsters production stopped, and the focus was on producing Model S. to begin Model S production. Tesla bought a 5.3-million-square-foot plant from Toyota for $42 million The Roadster had three versions in 2007, 2011, and 2013 but the Model S luxury sedan came with a different structure. Model S had its battery pack underneath, which created more space at the rear compared to Roadster. This structure improved its handling due to its low center of gravity. The 2012 car is aimed at entering the mainstream consumer market, unlike the Roadster which is seen as the billionaire's play toy. The model received several automotive and environmental awards, , which also named it “the best car ever tested.” a near-perfect score of 99 out of 100 from Consumer Reports Superchargers station, its first freestanding charging station, was introduced on September 24, 2012, with six stations opened in California. Today, it operates more than 45,000 supercharger stations at over 5,000 sites worldwide, including 1,966 in North America, 985 in Europe, and 2,069 in the Asia/Pacific region. From 2014, autopilot drive was available in Model S and subsequent products. released the Model X, a vehicle built on a car chassis but with features of a sport-utility vehicle in 2015. The crossover vehicle has a maximum battery range of about 340 miles (547 km) and seats up to seven. Tesla In April 2015, Tesla entered the energy storage market, unveiling its Tesla Powerwall and battery packs. The company received orders valued at $800 million within a week of the unveiling. Tesla bought Solar City in August 2016 for $2.6 million and entered the photovoltaics market. From this moment, they could directly make their equipment. The Roadster maker launched the Model 3 as an affordable sedan for the mass consumer market in 2016. Tesla aimed to own a product line of Model S, E, X.  Originally named Model E, Model 3 became its official name because another company threatened to sue them over rights. . Model 3 became an all-time bestselling plug-in electric car worldwide and the first to sell 1 million units globally in June 2021. Musk, excited by the sales prospect of the car, announced that the second half would deliver 200,000 vehicles in 2017 Tesla opened its first Gigafactory in 2016 outside Reno, Nevada. Gigafactories are where Tesla produces its batteries and vehicles. It built these large factories in Buffalo, New York, and Shanghai, China, and more Gigafactories were planned. Tesla batteries can last 300,000 to 500,000 miles, with major companies now using them for their projects. Grohmann Automation was acquired for 109.5 million in January 2017. Tesla also acquired companies such as Riviera Tool, Perbix, Compass Automation, Hibar Systems, and German ATW Automation for their automation expertise, along with Maxwell Technologies and SilLion for battery technology. In 2017, because they’re no longer just a car company; they’re a provider of energy solutions across many sectors. They deal with electric vehicles, battery energy storage systems, and solar power generation. Tesla Motors changed to Tesla Conflict and Lawsuits Tesla has faced over 1000 lawsuits. Eberhard’s got a fight in him. He sued Tesla for forcing him out of the company, including libel and slander, in June 2009. . The company blamed supply chains. It soon became the bestselling electric car. Musk's failed prediction of Model 3 delivery cost them 5%, that’s over $2 billion in 2017 as investors began to dump their stocks Further problems came for Tesla in August 2018 when Musk tweeted that he was planning to take the company private at $420 and that he’s secured funds for that. Investors began to trade their stock before the privatization buyback occurred. The stock price went up by 10%. And SEC fined the company and Musk for misleading information and ruled that Musk's tweets must now pass through the company’s attorney. The company removed Musk as Director of the Board, and Robyn Denholm took over, but Elon remained Tesla’s CEO. Cost of Tesla and Vision 2020 Tesla released another crossover, the Model Y, in 2020. The Model Y was smaller and less expensive than the Model X and shared many of the same parts as the Model 3. Tesla Semi Truck came into the market in 2022. PepsiCo, Disney, Mecca, and Sons are among its customers. The electric trucks can go 0-60 mph in five seconds without cargo and in 20 seconds while carrying 80,000 pounds of cargo. It has an innovative cabin design with impact-resistant glass. And can travel 400 miles on a 30-minute charge. Tesla Cybertruck is an upcoming all-electric pickup soon to be released in 2023. The under $40,000 exoskeleton design truck will come in three variations, with battery ranges of 250, 350, and 500 miles. The truck's speed is 0 to 60 mph in 2.9 seconds. Tesla plans to release a new version of its classic Roadster model, offering a 0-60mph time of 1.9 sec with 737 lb-ft of torque. Cost Of Tesla’s Vehicles $88,490, Plaid $108,490. Model S Model 3 $53,240, LR $47,240 Wheel Drive $40,240 Model X $98,490, Plaid $108,490 Model Y LR $50,490, Performance $54,490, All Wheel Drive $47,740 Tesla’s Annual Revenue Since 2008 2008    $15 million 2009    $112 million 2010    $117 million 2011    $204 million 2012    $413 million 2013    $2.01 billion 2014    $3.2 billion 2015    $4.05 billion 2016    $7 billion 2017    $11.76 billion 2018    $21.46 billion 2019    $24.58 billion 2020    $31.54 billion 2021    $53,823 billion 2022    $81.46 billion Bottom Line The number one electric vehicle company now faces competition from BYD, General Motors, Ford, and Mercedes-Benz as the shift to greener vehicles continues. Tesla has changed how cars are made yet has not slowed down on its innovations. The company’s market cap, which once hit the trillion dollar mark, will be back there in a few months as we’re sure of an eventful 2023.

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