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Last Updated: Apr 25, 2025 | Study Period: 2024-2030
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.
Seat settings, steering & mirror adjustments, riding or driving style, usage of accelerator, brakes, clutch, and other parts can describe the type of driver â 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 to avoid any incidents with other vehicles, pedestrians, and infrastructure.
In a vehicle, the data is generated by a carâs 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 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 to identify concrete societal expectations and problems and map them with mitigating technological availability in the domain of autonomous driving and smart mobility.
An event Data Recorder for Autonomous Driving (EDR/AD) is an envisioned subsystem of a vehicular Controller Area Network that ensures the confidentiality, integrity, and availability of data related to the operation of a vehicle to permit recovery of the 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 supercomputers 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 technologiesâcameras, LiDAR, infrared, and GPS.
The brain of the autonomous vehicleâa computer system known as the neural networkâmust be exposed to as much of that data as possible 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 have various levels of autonomy which require training integrations on various categorizations and centralization methodologies.
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.
Automotive Data Storage Market can be segmented into the following categories for further analysis.
The Potential for large-scale deployment in the short or medium term within 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 traï¬c management with minimal human intervention promises the ï¬rst 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 (traï¬c signs, traï¬c 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 ï¬ows among internal components to their function and operation â presenting considerable technical, regulatory, security, and safety challenges. Collaborative intelligent transport (C-ITS) system, where data is being exchanged between vehicles and can inï¬uence 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 are instrumental for reconstructing traï¬c events and understanding what caused them, as well as for continuous improvement of technology.
In the above data, considerations for data requirements were given about the increasing levels of autonomy in the vehicular system. Apart from these, the regularised vehicular system with maximum human intervention also requires data storage within the vehicular access.
Persistent data storage in automotive systems can be seen as a trivial function for storing favorite radio stations, seat, light, rearview mirror adjustment, mileage, average fuel consumption, calibration data, all the way up to safety-critical data, such as pictures from cameras, radars, and another sensor in the moments before the 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 is 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 vehicleâs 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 todayâs 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 include Western Digital, Seagate, Dell, IBM, Nvidia, and Kioxia.
These companies are extensively involved in the development of new library structures and improvised integration techniques within the onboard system of automobiles to have better levels of compliance with 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 the way towards for better autonomy maintained in the future and capability of increased scrutiny in aspects of accidental data.
| 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 the US, EU and China |
| 14 | COVID-19 impact on overall market |
| 15 | COVID-19 impact on Production of components |
| 16 | COVID-19 impact on the point of sale |
| 17 | Market Segmentation, Dynamics and Forecast by Geography, 2024-2030 |
| 18 | Market Segmentation, Dynamics and Forecast by Product Type, 2024-2030 |
| 19 | Market Segmentation, Dynamics and Forecast by Application, 2024-2030 |
| 20 | Market Segmentation, Dynamics and Forecast by End Use, 2024-2030 |
| 21 | Product installation rate by OEM, 2023 |
| 22 | Incline/Decline in Average B-2-B selling price in the past 5 years |
| 23 | Competition from substitute products |
| 24 | Gross margin and average profitability of suppliers |
| 25 | New product development in the past 12 months |
| 26 | M&A in the past 12 months |
| 27 | Growth strategy of leading players |
| 28 | Market share of vendors, 2023 |
| 29 | Company Profiles |
| 30 | Unmet needs and opportunities for new suppliers |
| 31 | Conclusion |
| 32 | Appendix |
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