Global Automotive Data Storage Market 2023-2030

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    Adding safety features into cars and making them increasingly autonomous are rapidly creating a big data problem. More sensors produce more data, which has to be processed, moved, and ultimately stored somewhere in those vehicles. Car-borne data storage is becoming a vital aspect of cars, trucks, and lorries as they get smarter.


    Intelligent vehicles will use and generate a lot of data that needs storage inside the vehicle. The higher the autonomy level, the more IT is needed, and the more data that needs to be stored.


    In addition to data on components, products and vehicles, data is equally critical during the entire vehicle life, when it is playing on the road.


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    Seat settings, steering & mirrors adjustments, riding or driving style, usage of accelerator, brakes, clutch, and other parts can describe the type of driver – whether timid or aggressive. Such information, in turn, can be used to determine the wear & tear of components and the expected remaining life.


    Big Data and Data Analytics are commonplace during present times when a large amount of information can be collected, examined, reviewed, and processed for enhanced results. When a product is under development, various teams work on its different aspects.


    Advanced driver assistance systems (ADAS) collect data through various sensors in the vehicle using cameras, radar, and Lidar, and enable the vehicle to automatically respond to the environment in order to avoid any incidents with other vehicles, pedestrians, and infrastructure.



    In a vehicle, the data is generated by a car’s sensors, which can be outward-looking, like cameras, radar and lidar instruments, and also inward-looking, such as logged engine output, exhaust emissions and suspension spring rates. The Data Storage Capability onboard the Vehicle depends upon the mechanism being integrated into the vehicular requirements.


    This can basically be centralized, distributed or some combination of the two. A distributed design will need storage for each distributed computing element. A centralized scheme will have a central data storage facility, and a hybrid scheme will have a smaller central facility, and various storage elements in sub-systems around the car.


    The Policy Scan and Technology Strategy Design methodology is integrated in order to identify concrete societal expectations and problems and map them with mitigating technological availability in the domain of autonomous driving and smart mobility.


    Event Data Recorder for Autonomous Driving (EDR/AD) is an envisioned subsystem of a vehicular Controller Area Network which ensures the confidentiality, integrity and availability of data related to operation of a vehicle in order to permit recovery of exact situation following the occurrence of an event or on demand.


    AI can be trained by exposing it to vast sums of data, and autonomous vehicles are a particularly challenging application. So-called super computers are being embedded within these vehicles to perform complex calculations required to control the vehicle electronically. Part of this involves the fusion of various sensor and connectivity technologies—cameras, LiDAR, infrared and GPS.


    The brain of the autonomous vehicle—a computer system known as the neural network—must be exposed to as much of that data as possible in order to familiarise itself with those conditions. To have this data being utilized efficiently within the technological integrations of the Car Safety systems, the data needs to have possible interventions in the form of training analytics.


    The Automotive Vehicular Systems and Machinery has various levels of autonomy which requires training integrations on various categorizations and centralization methodologies.


    The vehicular autonomy is categorized into 6 levels of autonomy, ranging from 0 to 5, where Level 5 is considered a fully autonomous car in comparison to today’s Level 1 and Level 2 driver assistance available in many new vehicles.


    By 2030, ~20% of new cars sold globally could have at least Level 3 autonomous driving capability.1By that same year, it is estimated that 90 million connected and autonomous vehicles will each generate up to 10 Terabytes (TB) of data per day, or one Zettabyte(ZB) per day across the industry.


    When this autonomy is connected to AI, ML and DL, more data means more accurate algorithms. These algorithms must be continuously fed with new data and have performance requirements of hundreds of gigabytes per second.



    The Market of Automotive Data Storage can be segmented into following categories for further analysis.


    By Product Deployment Usage

    • Private Cloud
    • Public Cloud
    • In Vehicular Storage Unit


    By Vehicular Usage Type

    • Light Motor Vehicles
    • Light Commercial Vehicles
    • Specialised Vehicles
    • Heavy Commercial Vehicles


    By Application Type

    • Vehicle to Everything Application
    • Telematics Based Application
    • Infotainment Application
    • Autonomy Integration Application
    • Driving Experience Analysis Application
    • Edge Computing Application


    By Regional Classification

    • Asia Pacific Region – APAC
    • Middle East and Gulf Region
    • Africa Region
    • North America Region
    • Europe Region
    • Latin America and Caribbean Region



    The Potential for a large-scale deployment in short or medium term within the autonomous vehicular system-based technologies is self-driving technologies and smart mobility systems. This transition of conventional transport systems and infrastructure to collaborative intelligent transportation systems based on self-driving technologies, ubiquitous connectivity and traffic management with minimal human intervention promises the first integration of advanced AI.


    Collaborative intelligent transport system (C-ITS) is a peer-to-peer solution for the exchange of data among vehicles and other road infrastructural facilities (traffic signs, traffic controllers, other transmitting/receiving base stations or smart city sensors) without the intervention of network operator.


    The higher the automation of a vehicle, the more important are data flows among internal components to their function and operation – presenting considerable technical, regulatory, security and safety challenges. Collaborative intelligent transport (C-ITS) system, where data is being exchanged between vehicles and can influence their decisions, adds yet another layer of complexity to the already challenging problematics.


    Security and safety of autonomous vehicles and collaborative intelligent transportation systems is a strict overarching requirement for their market deployment and is a formidable technological and governance challenge.


    Real-time in-vehicle data recording, storage and access management is instrumental for reconstructing traffic events and understanding what caused them, also for continuous improvement of technology.


    In the above data, considerations for data requirements were given in reference to the increasing levels of autonomy in the vehicular system. Apart from these, the regularised vehicular system with maximum human intervention also has a requirement for data storage within the vehicular access.


    Persistent data storage in automotive systems can be seen as trivial function for storing favourite radio stations, seat, light, rear view mirrors adjustment, mileage, average fuel consumption, calibration data, all the way up to safety critical data, such as pictures from cameras, radars, and other sensor in the moments before accident.


    The most inclusions to the Storage System technology is the NAND based Flash Technology being used for automotive storage purposes. The development of car infotainment with ADAS and autonomous driving systems has increased the demand for NAND flash memory.


    With improved performance from 3D NAND, flash technology will be able to meet most of the stringent requirements for the next-generation automotive market.


    Another flash data-storage technology known as 3D NAND involves multi-layer silicon cutting, stacking memory cells to increase density, and allowing cells to span on each layer by reducing interference from adjacent cells.



    Today, the most common storage solution for in-vehicle infotainment (IVI) systems are Multimedia Cards embedded within the ECU of the Onboard electronic systems of the automobile .


    However, they cannot support autonomous driving capabilities that need sophisticated graphical user interfaces and faster speed to access models from memory. In addition, there must be adequate storage space in the vehicle’s infotainment system to store multimedia and high-resolution maps.


    As the automotive industry gets closer to fully autonomous vehicles and connected cars, the volume of data being produced, analyzed, and processed is exploding, especially at the edge. OEMs, suppliers, and software developers developing ADAS systems face the need for solutions that solve today’s problems while preparing them to remain competitive in the future.


    The major competitors in development as well as possible improvisation through continuous integration of new technological advancement in the Data Storage industry of the Automotive sector includes Western Digital, Seagate, Dell, IBM, Nvidia and Kioxia. 


    These companies are extensively involved in development of new library structures and improvised integration techniques within the onboard system of automobiles to have better levels of compliance to requirements.


    More advanced infotainment and ADAS systems. More storage for event data recording. Support for more 3D mapping. Entirely new applications and capabilities to take advantage of 5G, IoT and artificial intelligence are the few sectors of improvisation the companies are paving way towards for better autonomy maintained in the future and capability of increased scrutiny in aspects of accidental data.



    • Renesas ElectricalsWorking upon integration of Distributed architectures of storage across the automobile for autonomy and creation of ease of engineering perspective.
    • Western Digital Improvisation of centralized storage approach with virtualization and hypervisors, but some are still going to have these subsystems with different storage devices.
    • Wireless CarDevelopment of Cockpit Solution to vehicular cybersecurity for on board data storage disintegration.
    • Harman Working upon integration of Distributed architectures of storage across the automobile for autonomy and creation of ease of engineering perspective.
    • Seagate US Operational Working on massive in-vehicle storage capacity
    • Dell TechnologiesIntegration of high Density and High-Performance Storage Capacitance on board both Manual and Autonomy based vehicular systems.
    • KIOXIA Implementation of ADAS within future Autonomous Vehicles and High Scale Capacity accommodation.
    Sl no Topic
    1 Market Segmentation
    2 Scope of the report
    3 Abbreviations
    4 Research Methodology
    5 Executive Summary
    6 Introduction
    7 Insights from Industry stakeholders
    8 Cost breakdown of Product by sub-components and average profit margin
    9 Disruptive innovation in the Industry
    10 Technology trends in the Industry
    11 Consumer trends in the industry
    12 Recent Production Milestones
    13 Component Manufacturing in US, EU and China
    14 COVID-19 impact on overall market
    15 COVID-19 impact on Production of components
    16 COVID-19 impact on Point of sale
    17 Market Segmentation, Dynamics and Forecast by Geography, 2023-2030
    18 Market Segmentation, Dynamics and Forecast by Product Type, 2023-2030
    19 Market Segmentation, Dynamics and Forecast by Application, 2023-2030
    20 Market Segmentation, Dynamics and Forecast by End use, 2023-2030
    21 Product installation rate by OEM, 2023
    22 Incline/Decline in Average B-2-B selling price in past 5 years
    23 Competition from substitute products
    24 Gross margin and average profitability of suppliers
    25 New product development in past 12 months
    26 M&A in past 12 months
    27 Growth strategy of leading players
    28 Market share of vendors, 2023
    29 Company Profiles
    30 Unmet needs and opportunity for new suppliers
    31 Conclusion
    32 Appendix
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