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Data Science in Automotive Industry

Data Science
Automobile companies all over the world are moving towards a data-driven approach to produce better and safer vehicles. Data science offers better insights and mobility solutions for all with connected and autonomous vehicles.
Data Science in Automotive Industry

The rate of technology change continues at an incredible pace, irreversibly transforming how organizations operate. With self-driving vehicles, changing ownership, and elevated customer expectations, the automotive industry is on the leading edge of technology and reinventing how the world travels.

Let’s look at how data science is impacting the automotive industry.

Working with data in the automotive industry

The automotive industry’s maturity and broad reach allow companies to rebuild and innovate around data. One example is working with data across different data systems and types. Generally, data scientists prefer using tabular data similar to Excel. On the other hand, automotive data scientists have a much greater variety of data to work with. An excellent example of such data is raw instrumentation data, usually stored as a stream of hexadecimal digits.

Moreover, they may also come across data from intelligence systems in the form of sensor point clouds and images. Finally, automotive data scientists are also responsible for understanding why an autonomous vehicle behaves a certain way and how that varies among different models. Therefore, an automotive data scientist might merge point clouds with instrumentation data and join that to a set of tables.

Data Science is involved in every step.

  • Manufacturing: Automotive data scientists are responsible for selling only high-quality products. Engineers are required to test every vehicle individually, which can be time-consuming. Data scientists help by analyzing the entire population of suppliers, parts, and test data. Data can help analyze suppliers' financial performance, predict the delivery time, and use econometrics with regressions to study the monetary conditions of supplier locations.

  • Autonomous or self-driving vehicles: One of the hottest topics in the automotive sector is connected and autonomous vehicles. The operation of self-driving vehicles relies on sensor fusion algorithms and deep learning models. Data science plays a vital role in building these vehicles. It decodes IoT indicators like oil life monitors, battery charge monitors, and full diagnostics instrumentation into actionable insights. An excellent example of this is detecting pedestrians on the road and other safety systems in the vehicle for the driver and passengers. Autonomous and connected cars are equipped with sensors to detect where pedestrians are walking.

  • Sustainability initiatives: Sustainability is essential to all autonomous manufacturers. The government sets targets for fuel efficiency, but each auto company has its own goals. In addition, each vehicle offers different fuel efficiency, so data science is essential to optimize the fuel efficiency of a company’s entire line of vehicles. For example, a company can offer a gas-guzzling truck and an electric car in its product line; automotive data scientists can optimize the fuel consumption of the entire fleet while sticking to its global sales targets. Optimization measures can help auto manufacturers to claim government credits for fuel efficiency.

  • Revenue forecasts for vehicle financing: In the automotive sector, captive finance companies experience a high demand for financing inquiries. However, not every inquiry results in the closing of a contract. Therefore, financial service providers require steadfast planning. This leads to many questions, such as - how high is the probability that inquiries result in contracts? Or how much time passes between an inquiry and a closing? To answer these questions, several companies have modeled the probabilities of closing and the distribution of the duration until closing. For this model, companies use a data-driven approach. As a result, revenues can be predicted with daily accuracy.

Conclusion

Like in almost every other sector, data science significantly impacts the automotive industry. For example, companies like Tesla are heavily reliant on machine learning models and data science. Moreover, data science will probably be an integral part of the automotive sector. With auto manufacturers trying to innovate every day, I think it’s safe to say that we might witness a revolutionary product soon in the future.

To become a part of this fast growing industry, enroll for AlmaBetter’s Full Stack Data Science program along with 100% placement guarantee.

Arpit Mehar
Content Developer Associate at AlmaBetter

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