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How Machine Learning is Utilized in Autonomous Vehicles

Published on May 7, 2019 at 3:06 pm in Self-Driving Cars.

Reports regarding the technological advancement of self-driving cars are everywhere. In the recent past, autonomous vehicles could only be seen as prototypes in showrooms or in movies. With machine learning, however, self-driving vehicles are getting closer to regularly accessing roadways.

While there have been a number of accidents involving self-driving vehicles, advances in computer hardware and systems are aiming to reduce the number of people wrongfully injured by this new technology. It’s known that these unique vehicles are equipped with sensors, actuators, and controllers. The machine learning software, which continuously updates the vehicle’s surroundings and environment and predicts possible changes, is also an integral part.

What is Machine Learning?

Machine learning refers to the field of study that gives computers the ability to learn without the explicit programming to do so. The idea evolved from pattern recognition and the theory computers could learn from data to perform tasks without being told to do so. Essentially, the computer will be able to improve its own learning process based on experience without human assistance or interference.

With traditional learning, data is used to develop a program. The machine runs the program and the result is the output. With machine learning, however, the data and output are presented first. The machine creates its own program and continues to do so to reach the output.

Machine learning and artificial intelligence are sometimes used interchangeably; however, they are two very different terms. As discussed, machine learning refers to a machines ability to learn without being programmed to do as such. Artificial intelligence, however, revolves around adding intelligence to a computer or machine so it can do things that people can currently do better.

Machine learning algorithms can be separated into four categories. Regression algorithms develop image-based models for predicting and selecting driving features. Pattern recognition algorithms recognize patterns and rule out unimportant data points. Cluster algorithms discover structures from data points by detecting objects. Decision matrix algorithms are used for decision making.

Using Machine Learning with Self-Driving Cars

For machine learning to be successful with a driverless car, it’s important that the computer emulates the human brain. While not every driver is top notch, people learn to drive over time through a variety of different driving experiences. Non-verbal communication, like hand movements, eye contact, and even honking, allow drivers to communicate with each other. When a driver isn’t present, the computer needs to be able to acknowledge its surroundings and make informed decisions.

Some of the most common machine learning algorithms are being used to track objects, with the goal of improving how a self-driving car pinpoints objects and distinguishes between them. If the computer guesses incorrectly, the machine learning algorithm can modify the parts of the structure that make the mistake and keep making changes until accuracy is achieved.

The Benefits of Machine Learning and Maps

In order to navigate around areas successfully, especially crowded, urban locations, autonomous vehicles need up-to-date maps. The maps need to contain more detail and information than a basic GPS unit can store. In order to compress the mapping data and have the ability to download new maps automatically, machine learning can be used.

Edge Maps

Autonomous vehicle maps are generally created with LiDAR sensors and cameras. Information is gathered and stored locally, then it’s sent to a data center where it’s processed and uploaded. Edge-based algorithms, however, can allow a car to handle a majority of the processing. When the map can distill all the data into the bare essentials, this is called fingerprinting.

Fingerprinting

Digital fingerprints include the basic characteristics of a map. The extraneous details are left out. Information is captured in six dimensions, which allows the driverless car to know it’s exact position and orientation within four inches.

Greater Cognition

A driverless car’s cognition, or its ability to perceive and interpret its surroundings, can be improved with arm processors. These are the same processors that are used in many smartphones, network devices, and advanced driver assistance systems. When used, the arm processor can incorporate machine learning capabilities for speech recognition and computer vision to improve how the vehicle senses its surroundings.

While machine learning has the power to significantly reduce the number of accidents autonomous vehicles cause, the possibility for a crash still exists. Determining liability for a driverless car accident and ensuring you have the means to recover can be challenging. We pride ourselves in staying current in many areas of litigation, and we have the resources to answer legal questions regarding self-driving cars. Contact us for more information.

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Tad Thomas - Trial Lawyer

Tad Thomas

Managing Partner

Tad Thomas has dedicated his practice to representing plaintiffs in various types of civil litigation, including personal injury, business litigation, class actions, and multi-district litigation.

After graduating with his law degree in 2000 from Salmon P. Chase College of Law at Northern Kentucky University, Mr. Thomas immediately opened his own private practice and began representing injury victims.

In 2011, Thomas Law Offices was established in Louisville, Kentucky. Over the past decade, Mr. Thomas has expanded his firm and now has offices in three additional locations: Cincinnati, Ohio, Columbia, Missouri, and Chicago, Illinois. He is also a frequent lecturer on topics like trial skills and ethics and technology.

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