We can only begin to imagine the possibilities Artificial Intelligence holds, but one of the most well-known topics related to AI potential is that of autonomous driving. The concept of machines that mimic human cognition – Artificial Intelligence, or AI for short – dates back as early as ancient Greece, though the term wasn’t coined and developed into a field until 1956. In recent years the technology has rapidly progressed and its uses have broadened significantly to include areas like agriculture, medicine, voice assistance, and even autonomous driving.
The convergence of AI and autonomous vehicles is a big step for both the automobile industry and the AI industry. We can only begin to imagine the possibilities Artificial Intelligence holds, but one of the most well-known topics related to AI potential is that of autonomous driving. In recent years, major progress has been made by companies like Tesla, Waymo, and Alibaba towards the creation of fully autonomous vehicles powered by AI.
In this article, we will take a closer look at the use of AI in vehicles – from its role in infrastructure, to the algorithms that are equipping AI to take to the roads.
What are Autonomous Cars?
Autonomous cars are vehicles capable of performing the same actions as those driven by experienced humans without any physical input from humans. They can interpret obstacles and signs to move safely on their own. They listen for instructions and with the help of their sensors, machine learning systems, actuators, and complex algorithms, they carry out the command.
Artificial Intelligence has been the single biggest force of breakthroughs in the creation of autonomous cars. The first autonomous cars were unveiled in the 1980s, however, levels 4 and 5 of autonomous cars — which are fully autonomous — were made possible by AI.
To achieve autonomous driving, AI needs to plan and execute actions without the influence of a human driver. The AI is equipped to perform the same functions as a human driver. It has recognition and decision-making abilities, sensory functions, and the ability to model data with deep learning algorithms. Armed with these innovations, the AI-powered vehicle can perform autonomously.
Role of AI in Building Infrastructure for Autonomous Vehicles
For a car to be completely autonomous, it needs to have a camera to enable vision, be equipped with a communication system, and have sensors. These features are to enable the car to generate data with which it can function. The role of AI in creating this infrastructure for autonomous vehicles is to make those features exhibit the characteristics of a human driver. AI enables it to see, hear, think, and make decisions by itself using the data that has been gathered by using the components fitted in the vehicle.
How Should Autonomous Cars Make Life-or-Death Decisions?
There is a great deal of concern among drivers about the safety of riding in an autonomous car. The question of how autonomous vehicles keep passengers safe in a life or death situation is an important one that deserves some investigation. There are endless possible scenarios in which an AI could be forced to make such a decision. This is why rather than attempting to program an autonomous vehicle to react in a life or death situation, it is better to design it so that it can avoid this circumstance altogether.
This seems to be the approach many companies are taking while developing their autonomous cars, by taking measures such as training them to recognize obstacles and stop or go around them. There are AI-powered tools created to prevent accidents in conventional cars, such as blind-spot detection, where the driver is alerted if he is in another car’s blind spot, and Electronic Stability Control where the traction control bites into one to three wheels of the vehicle to prevent it from sliding out of control in risky weather conditions.
The Importance of GDDR6 to the Future of Autonomous Driving
GDDR6 memory is known for its higher bandwidth and speed than its predecessors. Its bandwidth is what runs the computer engine of AI systems, which are at the heart of the advancement of autonomous vehicles.
GDDR6 is also known for its ability to withstand the harsh conditions that vehicles on the road typically encounter. GDDR6 memory technology will certainly be playing an important role in the advancement of autonomous cars.
Artificial Intelligence used in autonomous vehicles will need to be able to “see” its surroundings, and this is accomplished using cameras, RADAR, and LIDAR. With the help of these sensors and cameras, installed in different parts of the car, the AI draws information from which to make decisions. Below are individual parts of the system and the role they play in data collection for autonomous cars.
Sensors
The sensors monitor the position of your car and how close they are to other vehicles, pedestrians, and objects on the road. The two sensors used by autonomous vehicles are the Light Imaging Detection and Ranging (LIDAR sensor) and the Radio Detection and Ranging (RADAR sensor).
The LIDAR sensor measures distances and identifies components of the road such as road marking and curbs by bouncing pulses of laser light off the car’s surroundings.
The RADAR sensor uses the same principles as the LIDAR sensor except that it uses radio waves. It contains an electromagnetic waves transmitter, an antenna for receiving and transmitting, a processor which determines the properties of objects, and a receiver. When the radio waves from the transmitter reflect off the object, it is then returned to the receiver and the information about the object is deduced by the processor.
The LIDAR and RADAR sensors have their advantages and disadvantages and are therefore best used in vehicles that best maximize their advantages.
Cameras are also known as image sensors in autonomous vehicles. They detect objects within their viewpoint, identify and classify them, then determine the distance between the car and the object.
The Importance of Data Annotation in Automotive AI Projects
The cameras and sensors may allow the car to ‘see’ its surroundings, but this visual ability would be useless without data annotation.
Data annotation is a crucial part of automotive AI projects because it allows objects to be identified. Once the visual information has been interpreted, it can be used. Without proper data annotation, the AI used in a car would be prone to accidents and unsafe to use. The higher the quality of the annotation, the higher the AI accuracy and the lower the chances are of crashing.
How Automotive Artificial Intelligence Algorithms are Used for Self-Driving Cars
Automotive AI algorithms are used for self-driving cars by using real-life data sets to train them. This training is what helps them develop the ability to make decisions based on what they have observed and learned.
Supervised vs Unsupervised Learning
Automotive AI algorithms can learn through supervised or unsupervised learning.
Supervised Learning – Interpretation of data based on training about how to decipher the data
Unsupervised Learning – The AI is left by itself to process the data it has received without any instructions or input on how to do so.
Since the classification of data is required in self-driving cars, supervised learning is the preferred machine learning method for autonomous cars.
Machine Learning Algorithms Used by Self-Driving Cars
Many machine learning algorithms can be used by self-driving cars, all of which can be classified into one or more of the following categories:
Regression Algorithms –- Good at predicting events by evaluating the relationship between two or more variables and comparing their effects on different scales
Decision Matrix Algorithms – Analyzes, identifies, and rates the performance of the relationship between value sets
Pattern Recognition Algorithms – These are also known as classification algorithms. They recognize patterns between data sets and classify them.
Cluster Algorithms – Discover structure from data points in cases where the image obtained is not easy to detect or was not classified by the pattern recognition algorithm.
Amongst the thousands of algorithms available in each of these categories, there are five that show the greatest potential for autonomous cars.
SIFT (Scale-Invariant Feature Transform) for Feature Extraction
This algorithm is a feature detection algorithm used to detect, describe, and match key points in a partially visible image/object. These key points are then used to identify the image/object in question.
AdaBoost for Data Classification
The AdaBoost algorithm is used both as a regression algorithm and as a classification algorithm, but the focus in this context will be on AdaBoost as a classification algorithm. When in use for data classification, it collects and classifies data to support the vehicle’s AI learning process. AdaBoost improves the AI’s decision-making abilities by grouping low-performing classifier data to get high-performing data.
The TextonBoost algorithm is similar in performance to the AdaBoost algorithm in that it combines low-performing classifiers to get one high-performing classifier. The major difference between TextonBoost and AdaBoost is in the fact that the former can interpret data related to appearance, context, and shape. By combining these three classifiers, the TextonBoost algorithm can more accurately recognize images and objects.
HOG is a feature descriptor just like the Scale Invariant Feature Transform Algorithm in that it is used to detect objects. It analyzes an image/object’s location to determine how it moves. It then breaks them into cells and computes each of the cells into a histogram of oriented gradients, normalizes the result, and returns a descriptor for each cell.
Even though HOG is not quite considered a machine learning algorithm because it is not linked to a particular algorithm, it is still very useful in machine learning.
YOLO is a machine learning algorithm based on the regression algorithm, used to identify and group objects. The YOLO algorithm detects objects in the AI’s line of vision and assigns them to groups. It then designates specific features to each set of objects that it has grouped, making it easier for the AI to recognize them.
Other Machine Learning Algorithms That Can Be Used
There are many other machine learning algorithms used in self-driving cars such as the K-means, the Principal Component Analysis, the Support Vector Machines, and more. Regardless of which are utilized, machine learning algorithms bring autonomous cars to life. Without the incorporation of these algorithms, AI cars would never have been made possible.
The public is watching closely as further advancements are made toward fully autonomous vehicles. With the progress that Artificial Intelligence has made in the last decade, it is only a matter of time until cars are safely driving themselves through streets across the world.
AI and Autonomous Driving
We can only begin to imagine the possibilities Artificial Intelligence holds, but one of the most well-known topics related to AI potential is that of autonomous driving. The concept of machines that mimic human cognition – Artificial Intelligence, or AI for short – dates back as early as ancient Greece, though the term wasn’t coined and developed into a field until 1956. In recent years the technology has rapidly progressed and its uses have broadened significantly to include areas like agriculture, medicine, voice assistance, and even autonomous driving.
Also Read: What are smart cities?
The convergence of AI and autonomous vehicles is a big step for both the automobile industry and the AI industry. We can only begin to imagine the possibilities Artificial Intelligence holds, but one of the most well-known topics related to AI potential is that of autonomous driving. In recent years, major progress has been made by companies like Tesla, Waymo, and Alibaba towards the creation of fully autonomous vehicles powered by AI.
In this article, we will take a closer look at the use of AI in vehicles – from its role in infrastructure, to the algorithms that are equipping AI to take to the roads.
What are Autonomous Cars?
Autonomous cars are vehicles capable of performing the same actions as those driven by experienced humans without any physical input from humans. They can interpret obstacles and signs to move safely on their own. They listen for instructions and with the help of their sensors, machine learning systems, actuators, and complex algorithms, they carry out the command.
Also Watch: A drone that can dodge anything thrown at it.
AI and Autonomous Driving
Artificial Intelligence has been the single biggest force of breakthroughs in the creation of autonomous cars. The first autonomous cars were unveiled in the 1980s, however, levels 4 and 5 of autonomous cars — which are fully autonomous — were made possible by AI.
To achieve autonomous driving, AI needs to plan and execute actions without the influence of a human driver. The AI is equipped to perform the same functions as a human driver. It has recognition and decision-making abilities, sensory functions, and the ability to model data with deep learning algorithms. Armed with these innovations, the AI-powered vehicle can perform autonomously.
Also Read: AI and Power Grids.
Role of AI in Building Infrastructure for Autonomous Vehicles
For a car to be completely autonomous, it needs to have a camera to enable vision, be equipped with a communication system, and have sensors. These features are to enable the car to generate data with which it can function. The role of AI in creating this infrastructure for autonomous vehicles is to make those features exhibit the characteristics of a human driver. AI enables it to see, hear, think, and make decisions by itself using the data that has been gathered by using the components fitted in the vehicle.
How Should Autonomous Cars Make Life-or-Death Decisions?
There is a great deal of concern among drivers about the safety of riding in an autonomous car. The question of how autonomous vehicles keep passengers safe in a life or death situation is an important one that deserves some investigation. There are endless possible scenarios in which an AI could be forced to make such a decision. This is why rather than attempting to program an autonomous vehicle to react in a life or death situation, it is better to design it so that it can avoid this circumstance altogether.
Also Read: AI in Traffic Management
This seems to be the approach many companies are taking while developing their autonomous cars, by taking measures such as training them to recognize obstacles and stop or go around them. There are AI-powered tools created to prevent accidents in conventional cars, such as blind-spot detection, where the driver is alerted if he is in another car’s blind spot, and Electronic Stability Control where the traction control bites into one to three wheels of the vehicle to prevent it from sliding out of control in risky weather conditions.
The Importance of GDDR6 to the Future of Autonomous Driving
GDDR6 memory is known for its higher bandwidth and speed than its predecessors. Its bandwidth is what runs the computer engine of AI systems, which are at the heart of the advancement of autonomous vehicles.
GDDR6 is also known for its ability to withstand the harsh conditions that vehicles on the road typically encounter. GDDR6 memory technology will certainly be playing an important role in the advancement of autonomous cars.
Also Read: Inside Arrival’s Futuristic Factory
How Autonomous Cars Generate their Data
Artificial Intelligence used in autonomous vehicles will need to be able to “see” its surroundings, and this is accomplished using cameras, RADAR, and LIDAR. With the help of these sensors and cameras, installed in different parts of the car, the AI draws information from which to make decisions. Below are individual parts of the system and the role they play in data collection for autonomous cars.
Sensors
The sensors monitor the position of your car and how close they are to other vehicles, pedestrians, and objects on the road. The two sensors used by autonomous vehicles are the Light Imaging Detection and Ranging (LIDAR sensor) and the Radio Detection and Ranging (RADAR sensor).
The LIDAR sensor measures distances and identifies components of the road such as road marking and curbs by bouncing pulses of laser light off the car’s surroundings.
The RADAR sensor uses the same principles as the LIDAR sensor except that it uses radio waves. It contains an electromagnetic waves transmitter, an antenna for receiving and transmitting, a processor which determines the properties of objects, and a receiver. When the radio waves from the transmitter reflect off the object, it is then returned to the receiver and the information about the object is deduced by the processor.
The LIDAR and RADAR sensors have their advantages and disadvantages and are therefore best used in vehicles that best maximize their advantages.
Also Read: Self taught AI will be the end of us
Cameras
Cameras are also known as image sensors in autonomous vehicles. They detect objects within their viewpoint, identify and classify them, then determine the distance between the car and the object.
The Importance of Data Annotation in Automotive AI Projects
The cameras and sensors may allow the car to ‘see’ its surroundings, but this visual ability would be useless without data annotation.
Data annotation is a crucial part of automotive AI projects because it allows objects to be identified. Once the visual information has been interpreted, it can be used. Without proper data annotation, the AI used in a car would be prone to accidents and unsafe to use. The higher the quality of the annotation, the higher the AI accuracy and the lower the chances are of crashing.
How Automotive Artificial Intelligence Algorithms are Used for Self-Driving Cars
Automotive AI algorithms are used for self-driving cars by using real-life data sets to train them. This training is what helps them develop the ability to make decisions based on what they have observed and learned.
Supervised vs Unsupervised Learning
Automotive AI algorithms can learn through supervised or unsupervised learning.
Since the classification of data is required in self-driving cars, supervised learning is the preferred machine learning method for autonomous cars.
Machine Learning Algorithms Used by Self-Driving Cars
Many machine learning algorithms can be used by self-driving cars, all of which can be classified into one or more of the following categories:
Amongst the thousands of algorithms available in each of these categories, there are five that show the greatest potential for autonomous cars.
SIFT (Scale-Invariant Feature Transform) for Feature Extraction
This algorithm is a feature detection algorithm used to detect, describe, and match key points in a partially visible image/object. These key points are then used to identify the image/object in question.
AdaBoost for Data Classification
The AdaBoost algorithm is used both as a regression algorithm and as a classification algorithm, but the focus in this context will be on AdaBoost as a classification algorithm. When in use for data classification, it collects and classifies data to support the vehicle’s AI learning process. AdaBoost improves the AI’s decision-making abilities by grouping low-performing classifier data to get high-performing data.
Also Read: AI to Pick Stocks
TextonBoost for Object Recognition
The TextonBoost algorithm is similar in performance to the AdaBoost algorithm in that it combines low-performing classifiers to get one high-performing classifier. The major difference between TextonBoost and AdaBoost is in the fact that the former can interpret data related to appearance, context, and shape. By combining these three classifiers, the TextonBoost algorithm can more accurately recognize images and objects.
Histogram of Oriented Gradients (HOG)
HOG is a feature descriptor just like the Scale Invariant Feature Transform Algorithm in that it is used to detect objects. It analyzes an image/object’s location to determine how it moves. It then breaks them into cells and computes each of the cells into a histogram of oriented gradients, normalizes the result, and returns a descriptor for each cell.
Even though HOG is not quite considered a machine learning algorithm because it is not linked to a particular algorithm, it is still very useful in machine learning.
Also Read: Top 10 AI books for beginners
YOLO (You Only Look Once)
YOLO is a machine learning algorithm based on the regression algorithm, used to identify and group objects. The YOLO algorithm detects objects in the AI’s line of vision and assigns them to groups. It then designates specific features to each set of objects that it has grouped, making it easier for the AI to recognize them.
Other Machine Learning Algorithms That Can Be Used
There are many other machine learning algorithms used in self-driving cars such as the K-means, the Principal Component Analysis, the Support Vector Machines, and more. Regardless of which are utilized, machine learning algorithms bring autonomous cars to life. Without the incorporation of these algorithms, AI cars would never have been made possible.
Also Read: Understanding Machine Learning: From Theory to Algorithms
Final Thoughts
The public is watching closely as further advancements are made toward fully autonomous vehicles. With the progress that Artificial Intelligence has made in the last decade, it is only a matter of time until cars are safely driving themselves through streets across the world.
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