The Science of Vehicle Data: Transforming Raw Device Data into Actionable Insights with Generative AIThe Science of Vehicle Data: Transforming Raw Device Data into Actionable Insights with Generative AI


February 29, 2024



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The Science of Vehicle Data: Transforming Raw Device Data into Actionable Insights with Generative AI

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Introduction to Vehicle Data Analytics

In the rapidly evolving landscape of the automotive industry, the importance of data analytics cannot be overstated. With the advent of software-defined vehicles and devices, as well as the Internet of Things (IoT), some automakers now have access to a wealth of data that was previously unimaginable. This data, when analyzed and interpreted correctly, can provide unprecedented insights into vehicle performance, usage patterns, and areas for improvement based on customer usage and operating conditions. However, knowing what raw device data to use and how to get actionable information out of it is not straightforward. It requires a robust understanding of the key concepts and technologies that underpin modern data analytics in the automotive context.

The challenge lies in capturing, storing, and analyzing the right data generated by vehicles on the road. A task that requires not only technical prowess but also a strategic approach to data management and analysis. With the right connected device solutions, automakers can turn raw data into a goldmine of insights that drive innovation, enhance customer satisfaction, and pave the way for the next generation of automotive advancements.

Engineering Challenges and Solutions

Turning raw vehicle data into actionable insights is a complex process that poses several engineering challenges. Firstly, the sheer volume of data generated by modern vehicles is staggering, with each software-defined vehicle producing as much as 19 terabytes of data per hour. This data encompasses everything from engine performance metrics to driver behavior patterns, and managing it requires sophisticated data storage and processing infrastructures, such as cloud-based platforms that can scale dynamically with demand.

Moreover, data collected from vehicles is highly diverse, including time-series data from sensors, binary data from electronic control units (ECUs), and diagnostic data. Engineers must employ advanced data integration techniques to create a unified view of this data. Additionally, depending on the amount of information being analyzed, engineers might need to utilize data normalization and aggregation, which enables the analysis of data across different vehicles and models, allowing them to identify trends and anomalies.

You may be wondering: where is the edge in all this? In a future blog, we will explore how the edge is moving closer to the source and the steps that need to be taken before it can be moved to the vehicle edge.

Advanced Analytics and Predictive Maintenance

At the heart of transforming device data into deeper actionable insights is the application of advanced analytics and machine learning algorithms. These technologies enable automakers to move beyond descriptive analytics (what happened) to predictive analytics (what will happen) and prescriptive analytics (what should be done). 

To leverage the full potential of generative AI, OEMs must dive deep into data science and machine learning, employing algorithms that can sift through vast datasets to identify patterns, trends, and anomalies. This requires a blend of domain technical expertise (ADAS, powertrain, batteries, etc) in addition to proficiency in statistical analysis, data modeling, and the ability to develop and train predictive models. Furthermore, integrating these analytics into the vehicle ecosystem demands knowledge of software engineering, especially in areas related to data ingestion, processing, and the secure transmission of data between vehicles and cloud-based platforms. 

Automakers have the option to leverage specialized solutions that not only facilitate advanced analytics but also make it accessible to those with domain expertise, circumventing the need for in-depth data science knowledge. Solutions like Sibros' Deep Connected Platform bridge the gap, offering a comprehensive software-defined vehicle ecosystem that simplifies the intricate data pipeline from collection through analysis to the derivation of actionable insights. This empowers domain experts and allows OEMs to concentrate on innovation and the creation of sophisticated vehicle functionalities without the prerequisite of deep technical expertise in data analytics. 

Use Cases for Vehicle Data Analytics

The application of vehicle data analytics is transforming the automotive industry, driving advancements in vehicle performance, safety, and customer satisfaction. Below, we delve into specific use cases, illustrating the power of data analytics today and peeking into the future possibilities.

Predictive Maintenance and Battery Health Monitoring

Battery Health Monitoring: In the realm of EVs, battery health monitoring is crucial. Traditionally, battery health was or is derived from vehicle data but incorporating additional data points, such as how the vehicle was charged and its operating conditions, can provide a more accurate representation of an EV’s battery state of health (SOH). Advanced analytics can scrutinize charge cycles, voltage fluctuations, and battery cell and pack temperature data to estimate battery lifespan accurately. This not only enhances the reliability of EVs but also supports warranty management and second-life battery applications. By predicting battery end-of-life, automakers can promptly alert owners to potential replacements, avoiding inconvenience and ensuring continuous vehicle operation. 

Predictive Maintenance: Predictive maintenance relies on the continuous monitoring of vehicle systems and components, such as the battery, engine, and transmission. By analyzing patterns in data like battery charge cycles, engine temperature, and gear shift timings, AI algorithms can predict potential failures before they occur. This approach moves maintenance from a reactive to a proactive stance, significantly reducing downtime and repair costs. It is also invaluable for electric vehicles (EVs).

Enhanced Service Optimization

Trip Analysis: Service optimization is another area where generative AI-driven vehicle data analytics shines. By examining detailed trip data, automakers can uncover patterns that may affect vehicle health, such as frequent short trips leading to premature battery degradation. This insight allows for targeted advice to vehicle owners, optimizing vehicle use and extending its lifespan.

Vehicle Off Monitoring: Continuous monitoring of vehicle systems, even when the vehicle is off, can reveal electrical components that fail to shut down properly. Identifying such issues early can prevent battery drain and extend the battery's life, showcasing the nuanced benefits of deep data analytics in vehicle maintenance.

Driving Behavior Analysis and Driver Training

Analyzing driver behavior through vehicle data offers a plethora of opportunities for enhancing safety, efficiency, and the driving experience. Metrics such as speed patterns, braking behavior, and steering inputs can inform personalized driver training programs, improving safety and reducing wear on the vehicle. 

Data-driven insights can also underpin driver scoring systems, both for consumer drivers as well as fleet drivers. This is where drivers receive scores based on their driving habits, which can be used to influence vehicle insurance rates, incentivize safer driving practices, and enable fleet managers to monitor and improve fleet performance.

Use Cases of Tomorrow

Energy Cost Optimization: Looking ahead, the analytical focus will shift towards optimizing energy costs associated with EVs. By analyzing charging patterns, energy consumption rates, and even utility pricing schedules, generative AI analytics can recommend the most cost-effective times and locations for charging, as well as help OEMs “right size” vehicle batteries based on actual customer usage. This not only promises to enhance the EV ownership experience but also aligns with broader environmental sustainability goals.

Tire Wear Monitoring: Future vehicle data analytics will also venture into predictive tire wear monitoring, using data on driving habits, vehicle load, and road conditions to forecast when tires need replacement. This ensures safety, optimizes maintenance costs, and enhances vehicle performance by preventing issues related to uneven tire wear.

Advanced Diagnostics and Trouble Not Identified (TNI): Vehicle diagnostics will also begin to leverage AI not just to identify known issues but to predict new ones, potentially identifying problems before they are physically detected. This advanced diagnostic capability will reduce warranty claim costs and enhance customer trust by ensuring issues are addressed promptly and accurately.

Leveraging Sibros for Transformative Vehicle Data Analytics

As we've explored the vast landscape of the role of generative AI and analytics in-vehicle data and their transformative potential for the automotive industry, the question arises: how can OEMs efficiently and effectively tap into this potential? 

This is where Sibros steps in, offering a comprehensive solution that empowers OEMs to harness the power of vehicle data for predictive maintenance, enhanced service optimization, driving behavior analysis, and more, paving the way for the use cases of tomorrow. Sibros offers a holistic and cutting-edge approach to vehicle data analytics, significantly accelerating the data-to-decision process by up to 90%. Its platform integrates and automates data curation, visualization, insights, and decision support, making it an invaluable tool for OEMs aiming to leverage advanced analytics for their unique needs and goals. 

With Sibros, OEMs can achieve rapid AI/ML model delivery, ensuring that business insights are obtained in seconds rather than days or weeks. This capability empowers OEMs to enhance vehicle performance, customer satisfaction, and operational efficiency across various departments, from R&D to customer service, by providing critical insights instantly and facilitating faster decision-making and innovation.

Unlocking the Science of Vehicle Data

As we stand on the brink of a new era in automotive innovation, the role of data analytics in shaping the future of mobility has never been more critical. Mobility is a hyper-competitive market and OEMs that use connected vehicle data to its fullest potential will be able to reduce costs and enhance the customer value and reason for purchase. Sibros stands at the forefront of this revolution, offering a comprehensive suite of solutions that transform raw vehicle data into actionable insights, thereby empowering OEMs to unlock unprecedented levels of efficiency, safety, and customer satisfaction. We invite you to discover the full potential of your vehicle data, book a demo today.

Steve Schwinke
Steve Schwinke
Steve Schwinke is the VP of Customer Engagement at Sibros, working closely with OEMs and Tier One suppliers to accelerate their connected vehicle solutions. He is a senior Connected Experience Executive who goes beyond the obvious solutions delivering impactful results by building highly effective teams utilizing design thinking and unleashing individual’s full potential. He holds a Bachelor of Science in Electrical Engineering degree from the University of Michigan (Ann Arbor) and Master of Science in Wireless Communication Systems degree from Santa Clara University, and has been granted 34 patents in the area of telematics and connected vehicles. His cultural values include obsessively building trust, delivering on commitments, constructive conflict and recognizing others.