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Over the last decade, big data has come to play a pivotal role in the automotive industry. Large datasets, sourced from vehicle sensors, telematics, and customer interactions, provide a wealth of information. It’s a huge market: according to Precedence Research, the global automotive data management market size was estimated at $2.19 billion USD in 2022 and is expected to hit around $14.29 billion USD by 2032. The sheer volume of this data allows for detailed analysis and insights that were previously unattainable.
For instance, data from in-vehicle sensors can be used for predictive maintenance. By analyzing patterns and anomalies in the data, it's possible to predict potential failures before they occur, leading to reduced downtime and maintenance costs. This is particularly important in fleets, where vehicle availability directly impacts business operations. For example: Sigma Technology shared a case study about leveraging machine learning algorithms to improve safety in the fleet of a Swedish truck manufacturer. They analyzed data from various sources like sensors and telematics systems, then created a machine learning system that used historical data on factors such as road quality, vehicle speed and temperature to model the durability of brake pads and predict when they might eventually fail. The data-driven insights from this model helped ensure to the safety and reliability of vehicles, reduce maintenance costs and ultimately increase the efficiency of their fleet.
Large datasets also help in optimizing supply chains. This analysis by BCP Business and Management explores how automotive companies are leveraging vast datasets to optimize logistics, streamline production processes, and improve inventory management, leading to reduced costs and faster delivery times. Additionally, these datasets can give companies a deeper understanding of consumer preferences, helping them develop more consumer-centric vehicles that better meet the needs of the market.
Another significant application is in the development of autonomous driving technologies. Self-driving vehicles rely on vast amounts of data from cameras, LIDAR, and other sensors to understand their environment and make safe driving decisions. An example of this is Tesla’s Full Self-Driving (FSD) technology, which released in v12 beta recently, which highlights the significance of retaining and analyzing vast amounts of training data over many years. Instead of controlling the vehicle with more than 300,000 lines of explicit C++ code as the previous version did, pre-programming for specific scenarios, this latest version of FSD makes a huge leap by using end-to-end neural nets trained on millions of video clips. Each new version of Tesla's FSD software builds upon the accumulated data from previous versions, making the dataset ever larger. This process requires substantial data storage capacity, as the continuous improvement of autonomous driving capabilities depends on analyzing and learning from extensive historical data.
As data becomes an indispensable component of the automotive industry, particular challenges have come to light - particularly around how to store all this data when it grows so quickly. Let’s imagine a scenario: a car manufacturer wants to build self-driving car systems on a cloud environment. What do they need from their data platform to make this a reality?
First things first: they need to develop machine learning models to drive the cars autonomously. The development of such models relies on vast datasets to train algorithms in recognizing patterns, making decisions, and predicting outcomes. This involves not just the raw sensor data but also labeled datasets for supervised learning. Moreover, for machine learning models to improve over time (as well as to comply with regulatory requirements), companies need to maintain extensive historical archives of data for retrospective analysis and auditing. In order to retrain their models with this historical data, the data platform needs to accomodate very large storage capacities.
Next, before they can be deployed in the real world, self-driving algorithms are rigorously tested in simulated environments based on video data, test tracks that replicate real-world scenarios, as most jurisdictions world wide do not allow self-driving vehicles in all public areas. These simulations themselves generate large amounts of data, especially when comparing multiple variations of the same scenario with different parameters. Storing the inputs and outputs of these simulations, which are essential for refining algorithmic performance and ensuring safety, demands even more extensive storage resources.
Once the autonomous vehicles are out in the wild, there’s the matter of the sensor and camera data. Self-driving cars are equipped with numerous sensors and cameras, producing video footage, LIDAR data, radar data, and various other types of sensor data from the vehicle's surroundings. Beyond sensor data, self-driving cars generate operational and telemetry data related to vehicle performance, usage patterns, and maintenance needs. In 2016, Intel estimated that a single self-driving car might generate 4TB of data each day - nearly the total amount produced by 3,000 people in a day. Storing this monumental volume of data for further processing, analysis, and long-term improvement of the driving algorithms requires still more storage capacity.
For our imaginary automotive company, is not simply a question of space, but also of flexibility. Even if they find a solution to storing all the data they have now, the long-term scalability of these storage solutions is another major concern. As the amount of data continues to grow exponentially (as it inevitably will as their powerful self-driving algorithms capture the imagination of the public), our automotive company will need a storage solution that can scale efficiently. However, achieving this scalability without incurring substantial costs or complexities in infrastructure can be challenging. Simply adding more storage hardware won’t do the trick - what they will ultimately need is smart, efficient storage designed from the ground up to grow elastically across cloud environments or on-premises.
UltiHash addresses the numerous challenges such a company would face through a groundbreaking new approach to hot storage. We use advanced deduplication techniques to reduce the storage footprint of large datasets by analyzing objects at the byte level and eliminating redundant data. This technology allows us to significantly decreases the amount of storage required. Our method is especially effective for datasets that undergo frequent updates, common in automotive telematics and sensor data.
Moreover, UltiHash is fundamentally designed for scalability. As automotive data grows, UltiHash's storage solution can expand seamlessly along with it, automatically increasing the hardware resources employed by the system depending on the load, adding more data nodes if needed. If there is a spike in request load, UltiHash will elastically fire up new resources to keep up with the required workload.
Most importantly, we achieve all this without compromising on high-speed data access, all too important for everyday data-intensive applications in the automotive industry like vision model training and manufacturing analysis. By ensuring that large volumes of data can be stored and accessed quickly, UltiHash is able to support the real-time processing needs of the modern automotive industry.
The future of the auto industry hinges on the ability to harness, interpret, and optimize the wealth of data generated by electric vehicles and autonomous cars. Optimizing the processes around this data - particularly the storage of ever-expanding datasets - will allow innovation will flourish. Over time, this has the potential to transform the way we perceive and interact with transportation, steering us towards a future where sustainability, efficiency, and autonomy converge to redefine the driving experience.