How MongoDB and Alibaba Cloud are Powering the Era of Autonomous Driving

Humza Akhtar and Arnaldo Vera
September 11, 2023 | Updated: February 21, 2024

The emergence of autonomous driving technologies is transforming how automotive manufacturers operate, with data taking center stage in this transformation. Manufacturers are now not only creators of physical products but also stewards of vast amounts of product and customer data. As vehicles transform into connected vehicles, automotive manufacturers are compelled to transform their business models into software-first organizations. The data generated by connected vehicles is used to create better driver assistance systems and paves the way for autonomous driving applications. It has to be noted that the journey toward autonomous vehicles is not just about building reliable vehicles but harnessing the power of connected vehicle data to create a new era of mobility that seamlessly integrates cutting-edge software with vehicle hardware.

The ultimate goal of autonomous vehicle makers is to produce cars that are safer than human-driven vehicles. Since 2010, investors have poured over 200 billion dollars into autonomous vehicle technology. Even with this large amount of investment, it is very challenging to create fully autonomous vehicles that can drive safer than humans. Some experts estimate that the technology to achieve level 5 autonomy is about 80% developed but the last 20% will be extremely hard to achieve and will take a lot of time to perfect. Unusual events such as extreme weather, wildlife crossings, and highway construction are still enigmas for many automotive companies to solve.

Check out our AI resource page to learn more about building AI-powered apps with MongoDB.

The answer to these challenges is not straightforward. AI-based image and object recognition still has a long way to go to deal with uncertainties on the road. However, one thing is certain, automotive manufacturers need to make use of data captured by radar, LiDAR, camera systems, and the whole telemetry system in the vehicle in order to train their AI models better. A modern vehicle is a data powerhouse. It constantly gathers and processes information from onboard sensors and cameras. The Big Data generated as a result presents a formidable challenge, requiring robust storage and analysis capabilities. Additionally, this time series data needs to be analyzed in real-time and decisions have to be made instantaneously in order to guarantee safe navigation. Furthermore, ensuring data privacy and security is also a hurdle to cross since self-driving vehicles need to be shielded from cyber attacks as such an attack can cause life-threatening events.

The development of high-definition (HD) maps to help the vehicle ‘see’ what is on the road also poses technical challenges. Such maps are developed using a combination of different data sources such as Global Navigation Satellite Systems (GNSS), radar, IMUs, cameras, and LiDAR. Any error in any one of these systems aggregates and ultimately impacts the accuracy of the navigation. It is required to have a data platform in the middle of the data source (vehicle systems) and the AI platform to accommodate and consolidate this diverse information while keeping this data secure. The data platform should be able to preprocess this data as well as add additional context to it before using it to train or run the AI modules such as object detection, semantic segmentation, and path planning.

MongoDB can play a significant role in addressing above mentioned data-related challenges posed by autonomous driving. The document model is an excellent way to accommodate diverse data types such as sensor readings, telematics, maps, and model results. New fields to the documents can be added at run time, enabling the developers to easily add context to the raw telemetry data. MongoDB’s ability to handle large volumes of unstructured data makes it suitable for the constant influx of vehicle-generated information.

MongoDB is not only an excellent choice for data storage but also provides comprehensive data pre-processing capabilities through its aggregation framework. Its support for time series window functions allows data scientists to produce calculations over a sorted set of documents. Time series collections also dramatically reduce storage costs. Column compression significantly improves practical compression, reduces the data's overall storage on disk, and improves read performance. MongoDB offers robust security features such as role-based access control, encryption at rest and in transit, comprehensive auditing, field-level redaction and encryption, and down to the level of client-side field-level encryption that can help shield sensitive data from potential cyber threats while ensuring compliance with data protection regulations.

For challenges related to effectively storing and querying HD maps, MongoDB’s geospatial features can aid in querying location-based data and also combining the information from maps with telemetry data fulfilling the continuous updates and accuracy requirements for mapping. Furthermore, MongoDB's horizontal scaling or sharding allows for the seamless expansion of storage and processing capabilities as the volume of data grows. This scalability is essential for handling the data streams generated by fleets of self-driving vehicles.

During the research and development of autonomous driving projects, scalable infrastructure is required to quickly and steadily collect and process massive data. In such projects, data is generated at the terabyte level every day. To meet these needs, Alibaba Cloud provides a solution that integrates data collection, transmission, storage, and computing. In this solution, the data collected daily by sensors can be simulated and collected using Alibaba Cloud Lightning Cube and sent to the Object Storage Service (OSS). Context is added to this data using a translator and then this contextualized information can be pushed to MongoDB to train models. MongoDB and Alibaba Cloud recently announced a four-year extension to their strategic global partnership that has seen significant growth since being announced in 2019. Through this partnership, automotive manufacturers can easily set up and use MongoDB-as-a-service-ApsaraDB for MongoDB from Alibaba Cloud’s data centers globally.

Graphic displaying how data collection and model training data link with MongoDB on Alibaba Cloud
Figure 1: Data collection and model training data link with MongoDB on Alibaba Cloud.

When the vehicle is on the road, the telemetry data is captured through an MQTT gateway, converted into Kafka, and then pushed into MongoDB for data storage and archiving. This data can be used for various applications such as real-time status updates for engine and battery, accident analysis, and regulatory reporting.

Graphic displaying how mass production vehicles data link with MongoDB on Alibaba Cloud. Vehicles connect to MQtt data gateway, which then connects to Kafka, then Data processor, and then to ApsaraDB for MongoDB.
Figure 2: Mass Production vehicles data link with MongoDB on Alibaba Cloud

For a company that is looking to build autonomous driving assistance systems, Alibaba Cloud and ApsaraDB for MongoDB is an excellent technology partner to have. ApsaraDB for MongoDB can handle TBs of diverse sensor data from cars on a daily basis, which doesn't conform to a fixed format. MongoDB provides reliable and highly available data storage for this heterogenous data enabling companies to rapidly expand their system within minutes resulting in time savings when processing and integrating autonomous driving data. By leveraging Alibaba Cloud's ApsaraDB for MongoDB, the R&D team can focus on innovation rather than worrying about data storage and scalability, contributing to faster innovation in the field of autonomous driving.

In summary, MongoDB's flexibility, versatility, scalability, real-time capabilities, and strong security framework make it well-suited to address the multifaceted data requirements and challenges that autonomous driving presents. By efficiently managing and analyzing the Big Data generated, MongoDB and Alibaba Cloud are paving the path toward reliable and safe self-driving technology.

To learn more about MongoDB’s role in the automotive industry, please visit our manufacturing and automotive webpage.

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Building AI with MongoDB: Unlocking Value from Multimodal Data

One of the most powerful capabilities of AI is its ability to learn, interpret, and create from input data of any shape and modality. This could be structured records stored in a database to unstructured text, computer code, video, images, and audio streams. Vector embeddings are one of the key AI enablers in this space. Encoding our data as vector embeddings dramatically expands the ability to work with this multimodal data. We’ve gone from depending on data scientists training highly specialized models just a few years ago to developers today building general-purpose apps incorporating NLP and computer vision. The beauty of vector embeddings is that data that is unstructured and therefore completely opaque to a computer can now have its meaning and structure inferred and represented via these embeddings. Using a vector store such as Atlas Vector Search means we can search and compute unstructured and multimodal data in the same way we’ve always been able to with structured business data. Now we can search for it using natural language, rather than specialized query languages. Considering that 80%+ of the data that enterprises create every day is unstructured, we start to see how vector search combined with LLMs and generative AI opens up new use cases and revenue streams. In this latest round-up of companies building AI with MongoDB, we feature three examples who are doing just that. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. The future of business data: Unlocking the hidden potential of unstructured data In today's data-driven world, businesses are always searching for ways to extract meaningful insights from the vast amounts of information at their disposal. From improving customer experiences to enhancing employee productivity, the ability to leverage data enables companies to make more informed and strategic decisions. However, most of this valuable data is trapped in complex formats, making it difficult to access and analyze. That's where Unstructured.io comes in. Imagine an innovative tool that can take all of your unstructured data – be it a PDF report, a colorful presentation, or even an image – and transform it into an easily accessible format. This is exactly what Unstructured.io does. They delve deep, pulling out crucial data, and present it in a simple, universally understood JSON format. This makes your data ready to be transformed, stored and searched in powerful databases like MongoDB Atlas Vector Search . What does this mean for your business? It's simple. By automating the data extraction process, you can quickly derive actionable insights, offering enhanced value to your customers and improving operational efficiencies. Unstructured also offers an upcoming image-to-text model. This provides even more flexibility for users to ingest and process nearly any file containing natural language data. And, keep an eye out for notable upgrades in table extraction – yet another step in ensuring you get the most from your data. Unstructured.io isn't just a tool for tech experts. It's for any business aiming to understand their customers better, seeking to innovate, and looking to stay ahead in a competitive landscape. Unstructured’s widespread usage is a testament to its value – with over 1.5 million downloads and adoption by thousands of enterprises and government organizations. Brian Raymond, the founder and CEO of Unstructured.io, perfectly captures this synergy, saying, “As the world’s most widely used natural language ingestion and preprocessing platform, partnering with MongoDB was a natural choice for us. This collaboration allows for even faster development of intelligent applications. Together, we're paving the way businesses harness their data.” MongoDB and Unstructured.io are bridging the gap between data and insights, ensuring businesses are well-equipped to navigate the challenges of the digital age. Whether you’re a seasoned entrepreneur or just starting, it's time to harness the untapped potential of your unstructured data. Visit Unstructured.io to get started with any of their open-source libraries. Or join Unstructured’s community Slack and explore how to seamlessly use your data in conjunction with large language models. Making sense of complex contracts with entity extraction and analysis Catylex is a revolutionary contract analytics solution for any business that needs to extract and optimize contract data. The company’s best-in-class contract AI automatically recognizes thousands of legal and business concepts out-of-the-box, making it easy to get started and quickly generate value. Catylex’s AI models transform wordy, opaque documents into detailed insights revealing rights, obligations, risks, and commitments associated with the business, its suppliers, and customers. The insights generated can be used to accelerate contract review and to feed operational and risk data into core business systems (CLMs, ERPs, etc.) and teams. Documents are processed using Catylex’s proprietary extraction pipeline that uses a combination of various machine learning/NLP techniques (custom Named Entity Recognition, Text Classification) and domain expert augmentation to parse documents into an easy-to-query ontology. This eliminates the need for end users to annotate data or train any custom models. The application is very intuitive and provides easy-to-use controls to Quality Check the system-extracted data, search and query using a combination of text and concepts, and generate visualizations across portfolios. You can try all of this for free by signing up for the “Essentials'' version of Catylex . Catylex leverages a suite of applications and features from the MongoDB Atlas developer data platform . It uses the MongoDB Atlas database to store documents and extract metadata due to its flexible data model and easy-to-scale options, and it uses Atlas Search to provide end users with easy-to-use and efficient text search capabilities. Features like highlighting within Atlas Search add a lot of value and enhance the user experience. Atlas Triggers are used to handle change streams and efficiently relay information to various parts within the Catylex application to make it event-driven and scalable. Catylex is actively evaluating Atlas Vector Search. Bringing together vector search alongside keyword search and database in a single, fully synchronized, and flexible storage layer, accessed by a single API, will simplify development and eliminate technology sprawl. Being part of the MongoDB AI Innovators Program gives Catylex’s engineers direct access to the product management team at MongoDB, helping to share feedback and receive the latest product updates and best practices. The provision of Atlas credits reduces the costs of experimenting with new features. Co-marketing initiatives help build visibility and awareness of the company’s offerings. Harness Generative AI with observed and dark data for customer 360 Dataworkz enables enterprises to harness the power of LLMs with their own proprietary data for customer applications. The company’s products empower businesses to effortlessly develop and implement Retrieval-Augmented Generation (RAG) applications using proprietary data, utilizing either public LLM APIs or privately hosted open-source foundation models. The emergence of hallucinations presents a notable obstacle in the widespread adoption of Gen AI within enterprises. Dataworkz streamlines the implementation of RAG applications enabling Gen AI to reference its origins, consequently enhancing traceability. As a result, users can easily use conversational natural language to produce high-quality, LLM-ready, customer 360 views powering chatbots, Question-Answering systems, and summarization services. Dataworkz provides connectors for a vast array of customer data sources. These include back-office SaaS applications such as CRM, Marketing Automation, and Finance systems. In addition, leading relational and NoSQL databases, cloud object stores, data warehouses, and data lake houses are all supported. Dataflows, aka composable AI-enabled workflows, are a set of steps that users combine and arrange to perform any sort of data transformation – from creating vector embeddings to complex JSON transformations. Users can describe data wrangling tasks in natural language, have LLMs orchestrate the processing of data in any modality, and merge it into a “golden” 360-degree customer view. MongoDB Atlas is used to store the source document chunks for this customer's 360-degree view and Atlas Vector Search is used to index and query the associated vector embeddings. The generation of outputs produced by the customer’s chosen LLM is augmented with similarity search and retrieval powered by Atlas. Public LLMs such as OpenAI and Cohere or privately hosted LLMs such as Databricks Dolly are also available. The integrated experience of the MongoDB Atlas database and Atlas Vector Search simplifies developer workflows. Dataworkz has the freedom and flexibility to meet their customers wherever they run their business with multi-cloud support. For Dataworkz, access to Atlas credits and the MongoDB partner ecosystem are key drivers for becoming part of the AI Innovators program. What's next? If you are building AI-enabled apps on MongoDB, sign up for our AI Innovators Program . We’ve had applicants from all industries building for a huge diversity of new use cases. To get a flavor, take a look at earlier blog posts in this series: Building AI with MongoDB: First Qualifiers includes AI at the network edge for computer vision and augmented reality; risk modeling for public safety; and predictive maintenance paired with Question-Answering systems for maritime operators. Building AI with MongoDB: Compliance to Copilots features AI in healthcare along with intelligent assistants that help product managers specify better products and help sales teams compose emails that convert 2x higher. Head over to our quick-start guide to get started with Atlas Vector Search today. Finally, check out our MongoDB for Artificial Intelligence resources page for the latest best practices that get you started in turning your idea into AI-driven reality.

September 11, 2023
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MongoDB and Partners: Building the AI Future, Together

If you’re like me, over the past year you’ve closely watched AI’s developments—and the world’s reactions to them. From infectious excitement about AI’s capabilities, to impatience with its cost and return on investment, every day has been filled with AI twists and turns. It’s been quite the roller coaster. During the ride, from time to time I’ve wondered where AI falls on the Gartner hype cycle, which gives "a view of how a technology or application will evolve over time." Have we hit the "peak of inflated expectations" only to fall into the "trough of disillusionment?" Or is the hype cycle an imperfect guide, as The Economist argues? The reality is that it takes time for any new technology—even transformative ones like AI—to take hold. And every advance, no matter how big, has had its detractors. A famous example is that of Picasso (!), who in 1968 said, “Computers are useless. They can only give you answers.” (!!) For our part, MongoDB is convinced that AI is a once-in-a-generation technology that will enhance every future application—a belief that has been reinforced by the incredible work our partners have shared at MongoDB’s 2024 events. Speeding AI development MongoDB is committed to helping organizations of all sizes succeed with AI, and one way we’re doing that is by collaborating with the MongoDB partner ecosystem to create powerful, user-friendly AI development tools and solutions. For example, Fireworks.ai —which is a member of the MongoDB AI Applications Program ecosystem —created an inference solution that hosts gen AI models and supports containerized deployments. This tool makes it easier for developers to build and deploy powerful applications with a range of easy-to-use tools and customization options. They can choose to use state-of-the-art, open-source language, image, and multimodal foundation models off the shelf, or they can customize and fine-tune models to their needs. Jointly, Fireworks.ai and MongoDB provide a solution for developers who want to leverage highly curated and optimized open-source models and combine these with their organization’s own proprietary data—and to do so with unparalleled speed and security. “MongoDB is one of the most sophisticated database providers, and it’s very easy to use,” said Benny Chen , cofounder of Fireworks.ai. "We want developers to be able to use these tools, and we want to work with providers who enable and empower developers." Nomic , another MAAP ecosystem member, also enables developers with best-in-class solutions across the entire unstructured data workflow. Their Embed offering, available through the Nomic API , allows users to vectorize large-scale datasets for use in text, image, and multimodal retrieval applications, including retrieval-augmented generation (RAG), using only their web browser. The Nomic-MongoDB solution is a highly efficient, open-weight model that developers can use to visualize the unstructured datasets they store in MongoDB Atlas . These insights help users quickly discover trends and articulate data-driven value propositions. Nomic also supported the recently announced vector quantization in MongoDB Atlas Vector Search , which reduces vector sizes while preserving performance. Last—but hardly least!—there’s our new reference architecture with MAAP partners AWS and Anthropic. Announced at MongoDB.local London , the reference architecture supports building memory-enhanced AI agents, and is designed to streamline complex processes and develop smarter, more responsive applications. For more—including a link to the code on Github— check out the MongoDB Developer Center . Making AI work for anyone and everyone The companies MongoDB partners with aren’t just making gen AI easier for developers—they’re building tools for everyone. For example, Capgemini has invested $2 billion in gen AI and is training 100,000 of its employees in the technology. GenYoda, a solution that helps insurance professionals with their daily work, is a product of this investment. GenYoda leverages MongoDB Atlas Vector Search to analyze large amounts of customer data, like policy statements, premiums, claims history, and health information. Using GenYoda, insurance professionals can quickly analyze underwriters’ reports to make informed decisions, create longitudinal health summaries, and streamline customer interactions to improve contact center efficiency. GenYoda can ingest 100,000 documents in just a few hours and respond to users’ queries in two to three seconds—a metric on par with the most widely used gen AI models. And it produces results: in one example, by using Capgemini’s solution an insurer was able to increase productivity by 15%, add new reports 25% faster (thus speeding decision-making), and reduce the manual effort of searching PDFs, increasing efficiency by 10%. Building the future of AI together So, what’s next? Honestly, I’m as curious as you are. But I’m also incredibly excited. At MongoDB, we’re active participants in the AI revolution, working to embrace the possibilities that lie ahead. The future of gen AI is bright, and I can’t wait to see what we’ll build together. To learn more about how MongoDB can accelerate your AI journey, explore the MongoDB AI Applications Program .

November 4, 2024