How Data Science uses data to Develop Autonomous Driving

 Introduction

Nowadays, big data, machine learning (ML), and data science are becoming increasingly important, particularly in the automobile sector. In all spheres of interest, autonomous vehicles are a hot topic that interests both industry insiders and average consumers looking to enhance their vehicles. 

Automakers now have access to a variety of autonomous features thanks to technological breakthroughs, which have allowed them to drastically change the way cars operate by making them constantly online. Basically, cars may be used for more than just getting around; they can also be used to entertain us, make calls, shop, settle accounts, or even save lives.

Manufacturing no longer drives innovation in the automobile sector; now, data science does. The information obtained while building and operating vehicles can now be used to enhance all vehicle-related operations and bring driverless automobiles to the public. 

In this post, we'll delve deeper into advanced data science, big data, and ML topics while examining how these topics can be utilized to advance the idea of smart cities and enhance production, finance, product development, and consumer analytics in the automobile industry. Learn the ML techniques used in the automotive industry with the machine learning course in Bangalore

The Data Science Concept's Supporting Data

Sensors are now widely used in modern vehicles. According to a Mckinsey analysis from 2014, the global market for connection components was worth about $38 billion at the time and was expected to reach over $215 billion by 2020. This market is anticipated to reach $250 billion in 2022 and expand at a CAGR of almost 16% over the following two years.

According to experts, modern autos produce roughly 4,000 GB of data each day. To better understand where all of this information is coming from, let's take a careful look at the following image.

As you can see, modern cars are impossible without big data and the thousands of sensors collecting this enormous quantity of data. Data science is utilized in autonomous driving to make sure the vehicle does more than just get the driver from point A to point B; it also takes into account its surroundings.

Autonomous vehicles can develop ways to deal with potential problems on the road using all of this data. For instance, information exchanged from thousands of vehicles on the road can help drivers dodge traffic and other roadblocks and respond more quickly to some crises.

Sensors, processors, and actuators make up the three primary pieces of hardware in an autonomous vehicle. In most cases, the CPU receives data from the sensors and employs actuators to instruct the car on what to do. This contemporary vehicle is equipped with a plethora of sensors, ranging from LIDAR sensors, which can recognize road boundaries and markings, to ultrasonic sensors, which measure the position of objects nearby.

The ability to convert the driver's experiences into programmable knowledge will allow experts to make autonomous vehicles far more useful than ever. Of course, all of this data is used to improve the production process, increase finance, and transform conventional cities into smart cities in addition to serving the interests of the end user. 

Using Data in the Automotive Sector

• Production

Data science is quite helpful while creating a car. For instance, 96% accuracy may be achieved when predicting operator capacity using this ML-based approach. With the help of this data science solution, automakers can now efficiently plan their operator capacity, which will help to close any gaps between the real and desired number of operators. As a result, productivity increases, and the occupancy level is maintained consistently across all departments.

• Finance

Predicting VAT (value-added tax) codes is another way data science is used to save money. Orders are only ever made once for each model in the manufacturing process, more precisely in finance, but the manufacturing phase can extend many years. This one-time order creation is based on particular factors, such as the nation where the parts are manufactured, the nation where they are supplied, or the nation where various inventories and logistical hubs are located.

In this instance, the order's VAT code is automatically assigned. This code represents the VAT value as a percentage (%). The VAT code recorded in the system, however, does not vary over time, even when other factors (such as the country, the terms, etc.) might. Because of this, in some instances the VAT applied is erroneous, resulting in an improper amount of VAT paid to responsible institutions, which ultimately translates into monetary penalties and fines.

Based on data learned over the previous year, the machine learning model can be used to predict the VAT code each month (from a total of hundreds of thousands of observations). With a 90% accuracy rate, this solution may be used to compare the real VAT code with the projected one as well as forecast VAT codes. By paying penalties for improper VAT amounts paid, the automakers may save time and stay out of trouble with financial institutions thanks to this data science approach.

• Automation

Automation of data processing and information processing is another area where data science is incredibly useful. This conserves resources, which automakers can employ to take on tasks that have a higher added value. Large reports can be processed in under 30 or 45 minutes instead of over 10 hours thanks to data science.

• Smart Cities

Local governments and people can now employ data science to transform ordinary cities into intelligent ones. Cities can boost mobility for lower-income communities by using data science. Data can be utilized to reduce ownership costs while facilitating easy access to transportation.

For instance, automakers can offer businesses energy-efficient automobiles to serve rural regions through optimization algorithms. Moreover, reliability engineers and data scientists are collaborating to create vehicles that benefit specific areas.

Future smart cities can be shaped in 2023 with the use of appropriate data and ML. By enhancing traffic management, parking services, accident detection, road usage tax, emission management, and of course the infrastructure for electric vehicle charging, all the data gathered from autonomous cars may make the dream of a smart city a reality.

• Product Development

Data scientists working in the automotive industry use big data and data science to make sure that only high-quality autos are marketed. Even while engineers are capable of verifying each car's quality, this work needs to be done individually for each vehicle, which is where automation comes in.

The full population of parts, suppliers, and test data can be analyzed by data scientists. They carefully examine the financial performance of their suppliers, make predictions about their capacity to deliver on time based on prior performance and employ econometrics with regressions to examine the economic climate of the supplier regions. Know Condition-based preventative maintenance and fault prediction in depth by enrolling in the best data science course in Bangalore.

• Connected and Autonomous Vehicles

Deep learning models and sensor fusion techniques are essential for today's connected and autonomous automobiles. In order to transform IoT signs such as battery charge monitors, oil life monitors, and comprehensive diagnostics instrumentation into useful insights, data science is essential to the construction of these vehicles.

• Sustainability Initiatives

Currently, all auto manufacturers place a high value on sustainability. Governments set national or continental-level fuel efficiency goals, but every automaker also has its own objectives. Data science is required to optimize the fuel efficiency of a company's whole line of automobiles because every vehicle has a distinct fuel efficiency.

Automotive data scientists can therefore optimize to reduce the fuel consumption of the entire fleet while adhering to the company's global sales targets if a carmaker wants to include both an electric car and a pickup truck in its production line. The vehicle industry may be able to claim government tax credits for fuel economy thanks to these optimization efforts.

The application of data science in sustainability programs has three advantages: it's good for the environment, increases value for end users, and creates a possible source of income for staff.

Conclusion

I hope this article will be useful, the automotive industries use data science not only in one domain but also in different domains. Also, data scientists are more in demand for this industry as the automotive industry is showing tremendous growth these days. Becoming a data scientist with a domain specialization is made easy with the data science training in Bangalore, which gives you 15+ live projects for the live experience.