INTRODUCTION

Artificial intelligence (AI) refers to a computer's or a robot's capacity to carry out actions that are typically performed by intelligent beings. It involves the development of systems endowed with human intellectual characteristics, such as the ability to reason. Massive amounts of data are fed to the programmes and machines, which are then analyzed and processed to think rationally and carry out actions in a manner similar to a human.

A subset of artificial intelligence (AI) known as machine learning (ML) enables a machine to learn new skills and advance over time. Deep learning (DL) is a subset of machine learning that mimics human neural networks without the use of pre-processed input. Deep learning algorithms can ingest, process, and analyze enormous amounts of unstructured data, without any human involvement.

In this blog, we focus on their application in autonomous vehicles. Let’s get started.

ABOUT AUTONOMOUS VEHICLES

The basis for autonomous vehicles is to be able to drive like a human being. Being able to compute and take decisions as a human would but with a speed that is multifold as compared to humans. Therefore, it is necessary to provide these machines with the sensory, cognitive, and executive (memory, logical reasoning, decision-making, and learning) functions that people use while operating motor vehicles.

Autonomous vehicles are outfitted with

• Cameras

• sensors (LiDAR, Laser, Radar)

• Other communication systems

to generate massive amounts of data that, when combined with AI, enables the vehicle to see, hear, think, and make decisions in the same way human drivers do. AI is used in the central unit as well as in the multiple electronic control units (ECU) of the modern automobile.

COMPLETE DESCRIPTION OF THE SENSORS NEEDED

The three major sensors used by self-driving cars work in the same manner as human eyes and brains do. Sensors include cameras, radar and lidar. 

Collaboration of these offers the car with an accurate image of its surroundings. They help the car determine the position, speed, and 3D shape of objects in its surroundings. 

Capturing Images With Cameras

Autonomous vehicles may view their surroundings using high-resolution digital camera pictures. Self-driving automobiles are capable of "seeing" and interpreting ambient objects (such as signs, traffic lights, and animals) in ways comparable to human vision.

Cameras offer advantages associated with high-resolution imagery but do not work well in all weather types. Also, cameras only capture visible visual data.

Reading the Environment With Radar 

Radio detection and ranging is abbreviated as radar. Self-driving cars may send radio waves in particular directions using radar transmitters. The angles, ranges, and velocities of nearby objects may be ascertained by a car's radar receiver using reflected waves that return to it.

Radar detectors support the efforts of camera sensors at night or when visibility is poor.

Illuminating the World With LiDAR

Lidar (light detection and ranging) is a laser scanning method utilized by self-driving cars. A typical lidar sensor fires hundreds of infrared laser light beams into the air before waiting for the beams to reflect off nearby objects.

Lidar uses the speed of light to compute distances to objects. The longer it takes a lidar photodetector to get a return light signal, the further away an object is.

Lidar technology enables self-driving cars to recognise small objects with pinpoint accuracy. However, lidar is usually unreliable at night or in severe weather.

GETTING AROUND IN SELF-DRIVING CARS

As artificially intelligent technology, self-driving cars drive like humans to get from point A to point B. As a result, autonomous vehicles, like humans, rely on basic navigational skills:

Path Planning. 

Artificially intelligent autonomous vehicles use their sensor systems to plan routes through their environments.

Path planning is important in order to optimize the trajectory of the vehicle and to lead to better traffic patterns.  This can help reduce delays  and avoid congestion on the road.

Map Making and Reading. 

After the route has been planned, the car may navigate the road conditions by identifying objects, pedestrians, bicycles, and traffic signals in order to arrive at the destination. Object identification algorithms are a main focus of the AI field because they enable human-like behavior.

Self-driving cars use information from its sensor systems with other data (e.g. digital maps) to create and read maps of their environments.

Obstacle Avoidance. 

To navigate securely, self-driving cars employ real-time sensor systems. To drive, they must detect, analyze, and react to environmental cues effectively in order to avoid hazards such as people, bicycles, buildings, and other automobiles.

ML ALGORITHMS USED IN AUTONOMOUS VEHICLES

SIFT (scale-invariant feature transform) for feature extraction

Scale-invariant feature transform allows image matching and object recognition for partially visible objects. The algorithm uses an image database to extract salient points (i.e. keypoints) of an object. In a self-driving car, machine learning algorithms compare every new image with the SIFT features that it has already extracted from the database. It detects correspondence between them to identify objects. 

For instance, when an autonomous car sees a triangular road sign, it takes its three corners as keypoints. If a triangular road sign were damaged and bent back, a self-driving vehicle using SIFT would still recognize it based on its inherent features.

AdaBoost for data classification

AdaBoost is a decision matrix algorithm that ensures the adaptive boosting of learners. In essence, it takes the output of other regression and classification algorithms and checks how their performance corresponds to successful predictions. AdaBoost allows for more accurate decision-making and object detection in autonomous vehicles. It’s especially useful for face, pedestrian, and vehicle detection.

TextonBoost for object recognition

TextonBoost boosts image recognition based on the labeling of textons. Textons are clusters of visual data that have the same characteristics and respond to filters in the same way. TextonBoost actually enables self-driving cars to more accurately recognize objects. 

First, the computer creates a texton map of the image. Then it pairs features with textons and learns from contextual information. In this case, it learns that “cow” pixels are usually surrounded by “grass” pixels.

Histogram of oriented gradients (HOG)

Histogram of oriented gradients (HOG) is one of the most basic machine learning algorithms for autonomous driving and computer vision. It analyzes a region of an image, called a cell, to see how and in what direction the intensity of the image changes. Basically, HOG describes images as distributions of image intensity. Moreover, it’s inexpensive in terms of system resources. Self-driving cars can benefit from HOG as it can be a powerful initial step in the image recognition sequence.

YOLO (You Only Look Once)

YOLO (You Only Look Once) is a machine learning algorithm for classifying objects such as cars, people, and trees. YOLO analyzes the image as a whole and divides it into segmentsSince each class of objects possesses a set of features, YOLO labels objects according to them. The algorithm comes up with bounding boxes and predictions for each image segment. It considers each prediction in the context of the whole image and applies network evaluation only once. By contrast, other detection algorithms apply detectors and classifiers to multiple positions and regions of an image. 

That’s why YOLO is more accurate and faster than other machine learning algorithms. The YOLO algorithm is a great tool for object detection in autonomous vehicles. It ensures quick processing and vehicle response to real-world situations.

PROS AND CONS OF AUTONOMOUS CARS

Many people might believe that self-driving cars are still far from being a reality. In actuality, autonomous driving system development is already well under way. In reality, some level of automation is already present in the majority of current cars.

There have been various discussions regarding advantages and disadvantages of self-driving cars since we are dipping our toes into rather unexplored waters. For instance, the hotly argued Tesla AutoPilot pros and drawbacks discussion highlights concerns about security, regulation, and liability despite advertising ease and safety.

Here are some most commonly discussed pros and cons for self-driving cars:

PROS OF SELF-DRIVING CARS

1. Prevention of car crashes

With self-driving cars, major accident causes like drunk or distracted driving won't be issues. According to estimates, self-driving cars can cut down on collisions by up to 90%.

2. Societal cost-savings

According to reports, driverless vehicles can help society save almost $800 billion annually. There will be less burden on the healthcare system, more efficient transportation, and improved fuel savings among other savings.

3. Traffic efficiency

Autonomous cars can communicate in real-time, allowing them to travel at optimal distances from each other. Additionally, they determine the quickest route for you to take so as to avoid traffic.

4. Better Access and Mode of Transportation

Autonomous vehicles provide dependable transportation for people who are unable to drive or prefer not to. Using a self-driving car would be safe for elderly and disabled individuals. 

5. Environmentally Friendly

The environment is a crucial consideration in weighing the pros and cons of self-driving cars. Additionally, because self-driving cars will travel at steady speeds, continuous braking and acceleration will be reduced. All of these elements will help us reduce emissions and improve environmental sustainability.

 

CONS OF SELF-DRIVING CARS

1. Security Issues

If a large number of cars share the same network, however, they would be susceptible to a hack. Even a small hack could wreak significant damage on busy roads by causing collisions and gridlock traffic.

2. Job losses

With the advent of self-driving cars, those who rely on driving for a living may find their line of work obsolete. Truck drivers, bus drivers, and taxi drivers will all need to look for new jobs. Uber and fast food delivery drivers will also lose their jobs to driverless vehicles.

3. Initial Cost

Although long-term societal cost reductions from self-driving vehicles may be enormous, the initial cost of automated vehicles may be prohibitive. Of course, costs should decrease as new technology develops. But in the beginning, the entry barrier can be too high for the average person.

4. Moral Machine Dilemma

Another one of the disadvantages to self-driving cars is their lack of ability to make judgments between multiple unfavorable outcomes. For example, what if a self-driving car had to face a situation with only two possible options:

Veering to the left and striking a pedestrian

or

Veering to the right and hitting a tree, potentially injuring passengers inside your vehicle

Since both options are undesirable, which option would the autonomous car choose? The Moral Machine, developed by a group at MIT, is seeking to address this issue by collecting data on real-life people’s decisions. However, the data collected shows broad differences amongst different people groups, making it difficult to program any definitive answer for autonomous cars.

5. Machine Error

While most people believe that self-driving cars would probably reduce the number of accidents that occur, they do not entirely eliminate the possibility of accidents brought on by human error. 

 

FUTURE SCOPE

Blockchain technology is used in the cryptocurrency industry to build autonomous, transparent, and immutable systems using mathematical reasoning and algorithms. In the automobile industry, it is anticipated that the integration of blockchain technology and self-driving cars would result in a significantly improved autonomous system that will improve the transparency and precision of these cars' judgments.

The leading brands in automotive technology are working hard to integrate natural conversational AI into cars. This AI will make use of speech recognition, natural language understanding, speech synthesis, and smart avatars to improve user preferences and context understanding, as well as to increase comprehension of complex sentences and context. In the future, AI will be used to enhance vehicle performance, safety, and efficiency as well as to address environmental and health risks. Cars that can speak with one another and with other drivers may also be made using this technology.