Machine learning in self-driving cars is the way to use technology, whose scope of the application shows rapid expansion recently. Autonomous vehicles (also known as AV) are a “new black” in the world of navigation, long-distance trucking, and industrial and logistic automation as a whole. According to Globe Newswire, the global machine learning market is going to grow at a CAGR of around 40.1% over the forecast period 2019 to 2026. The market value of around US$ 76.8 Bn will be reached by 2026.
The variety of input data and possible solutions are becoming too massive for traditional pre-programmed systems. Therefore the current practical need for machine learning algorithms arisen pretty long ago.
Introducing machine learning to newcomers
Machine learning (abbreviated as ML) is a science direction, and more recently, technology solves the problem of computer training. By this, we mean the transfer to the hardware-software complexes of a purely limited set of knowledge with the possibility of their subsequent accumulation.
In this context, full-fledged training comparable to humans is not supposed. It is not at all what is called “with a deep understanding.” Thus, the computer acquires machine role knowledge. Machine knowledge does not allow you to make knowledgeable decisions comparable to human capabilities.
Machine learning solutions and areas of implementation
Machine learning is a subdivision of artificial intelligence designed to improve how a machine performs tasks. What’s important is that “learning” means that the machine goes beyond the training data. Devices managed with ML algorithms are capable of sensing its environment and act with little or no human input. Thus, machine learning implementation in autonomous driving is the way to increase moving safety and optimize cargo transportation.
Let’s overview some of the most popular applications of ML.
Virtual Personal Assistants
Virtual Personal Assistants are Apple Siri, Amazon Alexa, Microsoft Cortana, Google Home, etc. Digital aides have become one of the most significant finds of the 21st century. Machine learning algorithms have done phenomenal work in the field of speech recognition, natural language processing, text to speech and speech to text conversion. Once you ask them a question, they scan through the internet to find your relevant answers. Moreover, they also keep track of your schedule, goals, and preferences to recommend relevant information.
Image recognition and highly-generalizable functionality can enable several transformative user experiences. The technology identifies places, logos, people, objects, buildings, and several other variables in images. Image recognition introduces:
- automated image organization;
- user-generated content moderation;
- enhanced visual search;
- automated photo, video tagging;
- interactive marketing/creative campaigns.
Besides machine learning in image recognition, natural language processing, and self-driving cars, one of the most significant technology implementation areas is medical services. See how healthcare apps use ML:
- Quotient Health develops special methods for reducing the cost of supporting EMR (electronic medical records) systems, which also optimize how these systems are designed.
- KenSci applies ML for predicting disease and specifying treatment to help intervene earlier, predict population health risk by identifying patterns and surfacing high-risk markers and model illness progression, and more.
- Ciox Health uses machine learning best practices to enhance health information management and exchange of health information, modernize workflows, facilitate access to clinical data, and improve the accuracy and flow of health information.
From crafting personalized offers to fighting spam to enhancing search, machine learning delivers business value to social media platforms. The technology leverages big data sets to get insights and optimizes business processes. Facebook, Instagram, Twitter, and others already use ML-enabled solutions to reap these benefits.
Self Driving and Connected Cars
Machine learning implicated in autonomous driving minimizes failure and ensure safety. ML is accomplished through a fusion of many algorithms that overlap, interpret road signs, identify lanes, and recognize crossroads. Nissan presented a great recent example — Nissan car completed a 230-mile journey autonomously. It is the most extended and most complex such trip in the country. So let’s consider machine learning advantages in self–driving cars in more detail.
How is machine learning used for autonomous driving?
A connected car’s key idea is supplying useful information to a driver or a vehicle to make more informed decisions and safer driving. The use of a “connected vehicle” doesn’t imply that the car is making any driver’s choices. Instead, it provides information to the driver, including bypassing potentially dangerous situations.
One of the main objectives of any machine learning algorithm in the autonomous-driving car is a continuous rendering of the surrounding environment and the prediction of possible changes to the surrounding. These tasks can be divided into four sub-tasks:
- object detection;
- object Identification or recognition;
- object classification;
- object localization and prediction of movement.
Machine learning algorithms make autonomous cars capable of making real-time decisions, which increases safety and trust in autonomous vehicles. With machine learning algorithms, an onboard computer can apply induction and build knowledge structures. Especially where traditional programming fails, machine learning and artificial intelligence can succeed.
CHI Software and Connected Cars X case
The modern connected car is equipped with many sensors collecting diagnostic information on fuel consumption, braking performance, oil levels, tire pressures, emissions, light bulbs, and more. The place where we used to have only a car radio is now transformed into multi-functional “infotainment units” that provide maps, climate controls, music, or radio. These systems are integrated with the core sensors to collect engine performance, guide and notify drivers and warn of pending issues.
Connected cars can transmit data in batches, or in real-time, to a central collector. The collector is often a service running in “the cloud.” The data is analyzed and can be used to advise and guide car owners. The connectivity is used to download updates, such as maps, automatically.
An integral element of a Connected Car is the availability of a stable communication channel. If we consider the issue from this point of view, the first “connected” car was created by General Motors in collaboration with Motorola Automotive. The solution was called OnStar, and it entered the American market in 1996. When the OnStar system first became available as an option installed to the dealer, it was a discovery for the whole world. Thanks to analog cellular communications, the system offered an unprecedented opportunity to connect a car, leading to security and IT innovations.
In CHI Software Development Center, we work on the application of ML and AI algorithms for businesses that provide vehicle-related services for both passenger and freight transportation.
Our expertise covers data analysis that can be obtained directly from the vehicle and various devices installed on at the client’s request, such as an accelerometer, GPS, and others.
In the Connected Cars X case, among the others, we solved the following issues:
- The issue of definition of an individual driving style. We resolved the problem of “who is driving” (used in car-sharing and insurance).
- Assessment of driving aggressiveness (used in insurance and car-sharing).
- Comprehensive monitoring and control of the vehicle condition (taxi and car-sharing services).
- Preliminary planning of automotive services (from washing schedule to parts replacement).
- Movement control of the vehicle along the given route (transport companies).
Connected Cars X helps agents set smart coverage for aggressive drivers and help regular drivers save money on auto insurance. This solution consists of a Secure Telematics Hardware with OBD II interface, Secure Connected Cars X Platform, and a Mobile application.
Building the smart car of the future requires placing technology in the driver’s seat. It leads to the reasonable conclusion that machine learning algorithms, together with connected cars and autonomous vehicles, can build a new future in transportation. All related processes will be more safe, comfortable, and well-managed. So machine learning in autonomous driving is a reality we’ll face in the nearest future.