McKinsey stated, "By 2030, approximately 95% of new cars sold globally will be connected, compared to about 50% currently. Because automotive data offers numerous benefits, even players with vehicles that have lower levels of connectivity can begin to focus on the data."
ITTIA has created a solution to address the challenges of in-vehicle device data management, enabling automotive OEMs to unlock the full potential of this revolution. ITTIA DB enables real-time data acquisition, processing, and computation from in-vehicle devices.
Advantages of SDV data management
SDV generates an unprecedented amount of data from numerous sensors, ECUs (Electronic Control Units), and connectivity systems. OEMs and Tier 1 suppliers are seeking options to modernize their traditional data management methods to handle such massive amounts of data. They want flexible data management options that allow applications to be consistently embedded, deployed, and run on independently embedded devices within the vehicle, each with its own data. Let's look at some of the advantages of in-vehicle data computing:
Latency: Critical applications such as ADAS (Advanced Driver Assistance Systems) require lightning-fast decision-making. Sending data to the cloud for processing adds unacceptable latency.
Compliance with standards: The stringent regulatory nature of the automotive industry necessitates robust automotive product development practices to ensure compliance with a complex network of standards and regulations.
Bandwidth and Cost: The continuous transmission of large amounts of data incurs significant bandwidth costs and puts pressure on communication networks.
Security and Privacy: Automotive data is highly sensitive, and over-reliance on cloud connectivity increases the risk of data breaches and privacy issues.
Data ownership: OEMs and/or Tier 1 suppliers must own the data, from its generation and collection to its storage, analysis, and possible deletion.
Furthermore, rapidly gaining insights is a significant challenge for SDV architectures because in-vehicle devices must be intelligent. There is a direct link between intelligence and data management; to conduct data management activities to enhance competitive advantage and automate devices, data processing needs to run within the vehicle's devices. Similar to the Internet of Things (IoT), in-vehicle sensors, data sources, and devices generate vast amounts of raw data, much of which is essentially useless. Modern technologies like ITTIA DB enable these devices to transform raw data into usable information through data stream processing and querying capabilities, allowing embedded systems to gain insights and understanding of the system. Therefore, applications embedding ITTIA DB devices can choose how to collect, process, analyze, store, or transmit valuable information.
This support enables OEMs and Tier 1 suppliers to quickly build data-sensitive applications while maintaining strict control and ownership of the data inside the device.
In the era of SDV (Signaled Vehicle Vehicle) technology, sensors capture and transmit massive amounts of time-series data, turning cars into intelligent machines. Time-series data comprises a series of measurements or events that fluctuate over time, allowing for tracking and monitoring. In-vehicle data management software (such as the time-series database ITTIA DB) is designed to filter, downsample, and aggregate timestamped data, then store the results on each device. This makes it easy to retrieve data about the system's current state, trends, and past patterns. Applications can also manage concurrent input, enabling SDV devices to simultaneously store and process large data streams and facilitate integrated analytics.
The combination of time-series storage and stream processing allows for efficient indexing of data using timestamps; the timestamp linked to each data item serves as the primary input for computation. Furthermore, time-series data from in-vehicle connected devices forms a data stream that is continuously collected and flows into the database. Therefore, while the device performs instantaneous data computations, the overall data flow remains stable.
Key use cases for time-series data management and streaming embedded in SDV
The ability to process large amounts of data securely in real time helps meet a variety of automotive-specific use cases, including:
Predictive maintenance: Collects and analyzes data from the ECU to identify wear patterns and predict component failures, enabling timely and cost-effective maintenance.
ADAS Optimization: Local storage and processing of sensor data enhances ADAS functions such as obstacle detection, lane departure warning, and adaptive cruise control.
Personalized driving experience: Securely store driver preferences and usage behaviors in the vehicle to provide customized settings for seats, infotainment, and climate control.
Over-the-air (OTA) updates: Manage and track software versions and dependencies on individual ECUs to enable seamless and reliable software updates.
Fleet management and learning: Supports the selective sharing of insights from ITTIA database instances across vehicles to optimize fleet performance and train more powerful AI/ML models.
The ability to collect and analyze real-time data from in-vehicle devices enables manufacturers to optimize SDV (Surface Mount Vehicle) designs. As devices aggregate, analyze, and prepare data from sensors, vehicles benefit from granular data to improve reliability. Therefore, they can build applications to predict when components might fail. The processed data allows OEM and Tier 1 supplier applications to benefit from pre-failure maintenance, reducing accident risk and improving vehicle reliability. ITTIA DB offers significant value in terms of real-time data management performance, optimized storage, robust security, and adaptability to automotive SDV platforms.
