driverless vehicles

21st century has witnessed an unparalleled evolution in the automotive industry. From electric cars to self-driving engines, automobiles have raised consumer expectations. Needless to say, this would not have been possible without breakthrough technologies like machine learning, data science and more. Let’s take a closer look at their applications in the automobile industry to understand these technologies properly.

Every industry is using data to go from 0 to 100 swiftly because data works on facts and provides you with actual insights as to how you can achieve your goals with certainty in a very limited amount of time. Now data is just the fuel for the operation. The engine of this unprecedented advancement has emerged in the form of artificial intelligence and machine learning technology. In this article, we will discuss what is machine learning and how helps in facilitating the process of automation to such an extent that we are now thinking about using driverless vehicles as a means of transportation.

How Machine Learning (ML) Help to run Driverless Car

The name machine learning is very suggestive of the work that is facilitated with the use of this revolutionary technology. Machine learning techniques and algorithms help machines to learn from their past data processing experience and apply the learning to a new data set in a new setting. So basically equipping the machines with the ability to learn and develop in the process without any explicit programming is Machine Learning. It’s further broken down into two categories namely, Supervised Learning and Unsupervised Learning.  

Deep learning is one of the most widely used classes of machine learning that also helps in autonomous driving technology. Deep learning uses real-time data using feature learning to help the vehicles turn raw untamed complex data into actionable information.

Jumping Over to the Why & How of Driverless

We have often heard of artificial intelligence replacing many mundane and repetitive tasks. However, its application in the transportation industry through machine learning presents an entirely different scenario. If you have ever driven a vehicle, you must be familiar with the complexities on the road. How you cannot predict the movements of other cars and how at times you come across some insane drivers who don’t care about the general regulations. In addition to this, repairs at times, making you wait or alter your route due to scarcity of real-time information.

So why should one opt for a driverless vehicle? Since machines are different from human beings, they are there to complement human efforts, by achieving what could be impossible for a normal human being. Around 1.25 million people die in road crashes each year, such a waste of a precious human life and all because of some random human error. Driverless vehicles are better equipped to tackle this problem since there is almost no scope for any randomness with machines. Driverless vehicles will reduce the probability of road accidents to a negligible figure. Also, many of us drive our way to work, and this reduces the time we have in hand for leisure or other important tasks. Driverless vehicles would help in this regard by increasing efficiency and improving human productivity. 

You must be wondering how a car can manage to take all the complex factors into account and move from one place to another? Over time, machine learning technologies has developed such that it can comprehend all the probable variables with ease far better than a normal human being would ever do. The key here lies in machine learning algorithms that train a machine from various data- sets. The machine develops itself to apply the insights gained from the sample sets into unknown and more complex settings. Hence, unlike human beings, there is no chance of a random error. 

Let’s dig a little deeper into the functioning of a driverless vehicle. In the most basic sense, it can be understood as the vehicle that senses & evaluates its surroundings and environments and moves safely from one place to another with little or negligible human input. So to facilitate all this movement without human efforts, these vehicles are equipped with a variety of sensors, these include radar, LIDAR (Light Detection And Ranging), GPS, Sonar, Odometry, and inertial measurement units. 

on road sensing in driverless vehicle

In addition to this, it’s also equipped with advanced control systems to comprehend sensory information that helps in identifying appropriate navigation routes, hurdles in the path, and other relevant information to facilitate the movement. Briefly looking into the components discussed above, the LIDAR is used for Light detection and ranging, Odometry can be understood as the use of data from motion sensors to estimate the change in position over time. Sonar in automobiles helps by using an electromagnetic sensor in the front and back of the vehicle to estimate the proximity of the object with the car. We all are familiar with the use of GPS in determining the path and finding the best route to navigate. 

The important point to notice in the architecture of these driverless vehicles is that it requires machine vision to facilitate object visualisation and recognition. Here come the deep learning techniques: briefly, deep learning can be understood as a type of machine learning technique that deals majorly in neural networks, the formulation of a neural network is in turn inspired by the human brain. 

Machine Learning Algorithms in Route

The key applications of machine learning technology in autonomous vehicles includes localisation in space and mapping, sensor fusion and scene comprehension, evaluating driver’s behaviour pattern and state of mind. Some of the vital algorithms used for self-driving cars are as follows:

Scale-Invariant Feature Transform (SIFT) Algorithm

The scale-invariant feature transform algorithm in driverless vehicles helps to identify even partially visible objects with its image matching and object recognition feature. It extracts key points that help to identify objects and uses them for recognition. These key features include those that do not change with scaling, rotation or clutter. The algorithm compares new objects with those already stored in its database for better identification as per its past records. 

Adaptive Boosting Algorithm

The adaptive boosting or Adaboost algorithm helps to combine many weak classifiers to construct a strong one. In the case of autonomous vehicles, it helps in better decision making and object detection by taking into account multiple iterations to create one composite powerful learner. The assumption here is even the weak learners have different criteria for judgement and together will bring synergy into the equation. 

Histogram of Oriented Gradients (HOG) Algorithm

The histogram of oriented gradients algorithms forms the stepping stone for computer vision and autonomous vehicles. One of the most basic machine learning algorithms in use, it analyses a particular area from the image which is described officially as a cell. It analyses the dynamics of the changes in the image, to check how and in what direction the intensity of the image changes. In the most basic sense, it creates purposeful image gradients, which in a way are a compressed version of the original image. It is used in self-driving vehicles in the initial stages of the image recognition process. 

Hopping on the Trend

If we trace the idea of building autonomous vehicles it goes far back to the 1950s when the trials started to take place to bring this idea into existence. Ever since the ideation, there have been multiple companies who have shown interest in creating this technological marvel and a huge amount of money has been spent on research and development for the creation of a prototype to be tested before planting it on roads for the common public.

The automotive industry topped the list of sectors in which the R&D expenditure was the highest. Companies like Tesla, Alphabet, Volkswagen, Ford, General Motors, Toyota, and Daimler are some of the major players who invested in the R&D for the advancement of autonomous vehicles.

What once was merely a vision has now come into being. The idea of creating a vehicle that needs no manpower to operate is knocking on our doors. The vision has been further consolidated with the revolutionary machine learning technology that is fuelled by data and uses various algorithms to create a robust autonomous vehicle which will not only reduce the probability of accidents significantly but will also increase efficiency and human productivity. 

There are many more real-world applications of Machine Learning apart from driverless vehicles. All the advancement in the field are creating a demand for Machine Learning experts and a plethora of career opportunities have opened up for willing professionals. If you also wish to pursue a career in AI and Machine Learning, then upskill with Great Learning’s PG program in Artificial Intelligence and Machine Learning.



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