How Big Data Applications Impact The Future Of Self-Driving Cars?

 

Introduction

Big data is making great strides in self-driving car capabilities, driver safety, and improving the customer experience. Indeed, the international consultancy McKinsey & Co. predicts that big data in cars will become a $750 billion industry by 2030. This shows that big data will become the cornerstone of the driverless car of the future.

Ford is leveraging big data to improve the customer experience, operational reliability, and fundamentals of autonomous vehicles. Simply put, the power of big data does not give autonomous vehicles the power to think only. Autonomous cars will make their decisions based on the data they can collect.

How has big-data integration become popular in autonomous cars?

The available data is a critical factor that will affect the automation and motorization of self-driving cars. The problem with the data collected in cities is that it is highly differentiated and unstructured and that big-use methods and algorithms cannot process and analyze it. The data, including unique scenarios, videos, and images, will help learn driverless cars and provide a solid background for road decision-making.

In autonomous cars, this information is processed and analyzed in milliseconds by various built-in sensors. The car knows its place in the world, and it understands what is happening around it. In this way, it can not only drive safely from point A to point B but also pass on information about the road condition from the cloud to other vehicles. Self-driving cars are equipped with technology that detects and communicates vehicle position, speed, direction, and braking status. By connecting to a network, smart cars not only transmit information from smart sensors to the cloud but also react to conditions. 

Companies that collect and provide big data are Tier 1 and Tier 2 providers operating in the automotive industry. They detect traffic signs, the proximity of pedestrians, and dangerous objects on the road. They are working with the automotive industry to improve operations through 3D interfaces and projects ranging from interactive VR to helping automakers test new technologies to machine learning and training technologies that enable self-driving cars to become instinctive. These cars are based on computer vision, which uses a range of video cameras, radar, lidar, and laser light sensors that allow the car to sense the world around it.

Self-driving cars have been a hot topic for years, with countless start-ups and established car companies gearing up to tackle this piece of the AV puzzle. However, despite the extraordinary efforts of many major players in the automotive industry, autonomous cars are still not accessible outside special pilot programs.

Predictions of future self-driving vehicles based on big-data implementation

As level 4 and 5 AVs in our streets become commonplace, it is clear that more computing power will be needed, especially considering that low-level connected cars can now generate up to 25 gigabytes of data per hour. Road condition, signage, weather maps, forecasts from other cars on the road, and data from people and computers are processed while driving.

Those responsible for the expansionary development of the IoT are likely to do so, even if they become unavailable at some point in the future. Developing a networked network of tools supporting big data in the form of real collaboration, transparency, and information sharing will be a critical factor for the future of self-driving vehicles as big data continues to unfold. This network will allow autonomous cars to connect to existing networks and store the vast amount of data needed to navigate the roads - it is estimated that a self-driving vehicle can consume up to 40 Terabytes of data in eight hours of use.

The two technological marvels of big data and self-driving cars will continue to advance in the meantime, creating new opportunities and delivering on the promise of autonomous vehicles as a future opportunity. By 2040, 33 million driverless vehicles are projected to be on the road, and 55 percent of small businesses expect self-driving car technology to be deployed in the next two decades. The creation of AI-enabled future driverless cars will require low-latency wireless connections, fiber-optic networks, and data centers. The infrastructure for connectivity, storage, and data scarcity must also extend beyond the cars that drive.

Given data generation rates, the AECC sees self-driving cars creating a demand for additional infrastructure and new ways to sort data by value. One opportunity for big data to produce future autonomous cars is to create large networks of cars that can communicate with each other. These networks will interact with roadside wireless sensor networks to synchronize information about traffic lights and accidents. This feature will make the technology more reliable, and self-driving cars have the potential to become safer than human drivers and replace them in a not too distant future. By adopting this new sector, big data is being offered to pave the way for autonomous cars, especially in the PR department. 

Conclusion

To conclude, a solution capable of processing and analyzing the enormous amounts of data that self-driving cars gather is to make autonomous decisions on route choices. This data includes the car's position, speed, possible traffic jams, and information about the surroundings. One example is high-resolution mapping tools that increase self-driving vehicles' awareness of their surroundings on the road. Another solution is to use spatial big data in combination with Kafka and Spark architectures to collect unstructured data from different sources and to be processed in real-time.