The importance of data analysis in the
Development of Autonomous Vehicles

By: Sourav Chhabra July 25, 2020

The importance of data analysis in the development of autonomous vehicles

You may know that auto makers are already running tests on autonomous vehicles, with this new technology practically competing to be the leaders. In the last four years, global investors have already poured billions of dollars into self-driving car technology research and development, with autonomous vehicle technology funding outpacing the rest of automotive technology1. That is clearly a huge company.

In the race to meet what the Automotive Engineers Society (SAE) calls Level 5 autonomy — fully autonomous vehicles — manufacturers must face the complexities of teaching cars to drive faster than a human being. That drives time and expense in an already expensive journey. Although important to both protection and legal certification, there's no risk of failure here. However, that the time and expense of autonomous vehicle production is necessary in order to compete successfully in the race to put self-driving vehicles onto the market. When manufacturers want to keep up the pace, they need to look at new options.

Self-driving cars story

Cruise control was the first major automation of vehicles, which was patented of 1950 and is still used by most drivers to maintain their speed during long drives. Most modern cars already have several automated functions, such as proximity warnings and steering adjustment, which have been tried and tested, and have proven to be valuable features for safe driving. These technologies use sensors to alert the driver when they come too close to something that may be out of the driver's view or something that may not have been noticed by the driver.

The less roles drivers need to think about and pay attention to, the more they can concentrate on the road ahead of them and remain alert to hazardous situations that could arise at any time. Human error causes 90 per cent of all road accidents, which is one of the key reasons so many companies support autonomous vehicle growth. However, even if a driver is totally attentive, conditions outside their control, such as weather or other drivers, can cause them to leave the road or crash into other vehicles. Auto manufacturers are also working on the technology that is less than optimal for autonomous driving at temperature.

Data on autonomous vehicles

Autonomous vehicles combine a variety of sensors to perceive their environment including, among others, radar, lidar, computer vision, sonar and GPS. Such sensors interpret sensory information to navigate paths for identification, avoid obstacles and read appropriate markings, such as road signs. Autonomous vehicle development teams run tests in multiple locations around the world which take thousands of hours of test drive data. One eight-hour change will generate over 100 terabytes of data. To construct vehicle decision-making, this vast amount of data must be collected, offloaded, stored, and interpreted for algorithmic training.

The big challenge: How to handle all the data produced during the tests effectively and teach the vehicle how to make decisions faster under very different conditions ... including a moral dilemma. And how do you advise the vehicle to make an adjustment when an unforeseen problem in the real world becomes an occurrence that can change the actions of the vehicles?

The development of self-driving cars must effectively utilize a staggering amount of data

The development challenge for teams seeking SAE Level 5 autonomy is to collect and store a staggering amount of sensory data, and ultimately analyse and interpret that data to produce control systems that perceive information and accurately navigate the roads it encounters. Modern self-driving vehicle technology uses algorithms to combine sensor and other sources of data to drive reliably and without incident. It requires ongoing technology and emerging skills to integrate machine learning and artificial intelligence (AI) into building vehicle autonomy.

Role of big data in the development of autonomous vehicles

While they offered small steps towards automation, these developments remained milestones away from a fully automated car. Nevertheless, in the last decade, software firms have discovered the requisite programming to completely automate vehicles with the broad variety of developments that have been made in technology and the newly developed use of big data. Autonomous vehicles rely entirely on the data they collect via GPS, radar, and sensor technology, as well as the information they process via cameras.

Via these sources the knowledge cars collect provides them with the data required to make safe driving decisions. While car makers still use big data stores to work out the kinks of the thousands of scenarios in which an autonomous car could find itself, it's only a matter of time before self-driving cars disrupt the automotive industry by making up most of the cars on the road. As the price of sophisticated radars for such vehicles falls, self-driving cars will become more publicly accessible, which will improve road safety around the world.

Big data is changing industries all over the world, and profound learning contributes to progress towards fully autonomous vehicles. While it will still be many decades before self-driving cars are widely embraced, the transition will come slowly but gradually. We are likely to live in a time when vehicles are a safer mode of transportation in just a few decades, and accidents are disasters that are few and far between.

Examples of Data Science Uses

Additionally, here are few examples of how businesses are using data science to Novel in their sectors, create new products and make the world around them even more well-structured and Organized.

⦿ Healthcare Data Analytics

Data science has acquainted number of Development in the healthcare industry. With a broad network of data now available via all from EMRs to clinical databases to personal fitness trackers, medical professionals are finding new methods to understand disease, practice preventive medicine, diagnose diseases faster and search new treatment possibilities.

⦿ Self-Driving Cars and Data Mining

Tesla, Ford and Volkswagen are all executing portending analytics in their new wave of autonomous vehicles. These cars use thousands of mini cameras and sensors to reinforce real-time information. Using machine learning, predictive analytics and data science, self-driving cars can adjust to speed limits, avoid menacing lane changes and even take passengers on the fastest route.

⦿ Logistics Data

UPS turns to data science to escalate efficiency, both internally and along its carriage routes. There is tool which uses data science-backed statistical modelling and algorithms that create ideal routes for transportation drivers. The algorithm considers weather, traffic, construction, vehicular movement etc to suggest the best route. It is estimated that data science is economizing the logistics company up to 39 million gallons of fuel and more than 100 million delivery miles every year.

⦿ Entertainment

Do you ever amaze how Spotify just seems to suggest that perfect song you're in the mood for? Or how Netflix knows just what shows you’ll love to watch? Using data science, the music streaming can cautiously curate lists of songs based off the music genre or band you’re currently into. Netflix’s data assembler will recognize your need for culinary inspiration and recommend relevant shows from its huge collection.

⦿ Finance

Machine learning and data science have saved the financial industry millions of dollars, and quantitative amounts of time. Thanks to data science, what would take around 360,000 manual labor hours to complete is now finished in a few hours. Additionally, fintech companies like Stripe and Paypal are investing extensively in data science to generate machine learning tools that quickly detect and prevent fraudulent activities.

⦿ Cybersecurity

Data science is functional in each industry, but it may be the most influential in cyber security. Being able to detect and learn new methods of cybercrime, through data science, is essential to our safety and security in the future.