ML ALGORITHMS FOR CLEAN FUTURE

Unless you are living behind bars or under the rocks, you have heard of machine learning, autonomous driving, and self-driving cars. A couple of years before, what was seemingly just a piece of science fiction, is becoming a reality. What goes in the background of such unmanned ground vehicles? Machine Learning has the answer to this mind-boggling question. ML Algorithms are used for harnessing the potential for a clean future.

WHAT IS AN AUTONOMOUS VEHICLE?

An autonomous unmanned ground vehicle or a self-driving vehicle is a vehicle capable of driving by itself with negligible human intervention. The process of self-driving involves a continuous rendering of the vehicle’s surroundings and making possible changes based on data collected. To accomplish this task, the environment of a vehicle is monitored using industrial IoT i.e. IoT combined with other technologies such as ML, AI, Local Computing, etc.

LEVELS OF AUTOMATION

The Society of Automotive Engineers (SAE) International defines six different levels of automation that ranges from 0 (no automation) to 5 (full automation).

Level 0 - No automation

The complete control of the vehicle’s system rests with the driver. No interference from the system is allowed.

Level 1 - Driver assistance

The system can interfere with the speed and steering direction of the vehicle but still, the driver handles the majority of the controls.

Level 2 - Partial automation

The driver is no longer able to control the speed and steering direction of the vehicle. The driver must take control only if corrections are needed.

Level 3 - Conditional automation

The system takes full control over several functions like speed, steering, and monitoring of the environment. On the command of the system only, the driver must be ready to intervene. Partial distraction is also allowed for the driver such as checking text messages.

Level 4 - High automation

Human interference is no longer needed and the system takes complete control of the vehicle. The system can even abort the trip if needed, for example, parking a car.

Level 5 - Full automation

This level involves all the aspects of level 4 with a different environment. It requires the system to take control in all driving conditions. Research on more technological advancements is being carried out to achieve this level of automation.

MAJOR TASKS INVOLVED IN AUTOMATION

• Object Detection

• Object Identification

• Object Classification

• Object Localization

MACHINE LEARNING ALGORITHMS 

With the advancements of sensors in an ECU (Electronic Control Unit) in a car, it is essential to enhance the utilization of ML Algorithms to accomplish new tasks. Broadly, there are four categories of algorithms :

Clustering Algorithms

One of the major issues in the continuous monitoring of surroundings is low-resolution images. Sometimes, it is difficult to detect and locate objects on the grounds of low image clarity which then could result in inappropriate decisions. Clustering algorithms are good at discovering structure from data points. These algorithms describe the class of problems and a class of methods.

Ex: K-means

Regression Algorithms

Regression analysis is defined as the process of studying collected data by plotting observed points on a definite scale and then searching for the best possible algorithm to accomplish the task. The biggest challenge for any algorithm is to develop an image-based model to predict and apply required changes in the system controls.

Ex: Neural networks regression

Decision Matrix Algorithms

These algorithms are good at systematically identifying, analyzing and rating the performance of relationships between sets of values and information. The level of confidence of an algorithm decided the decision to be taken. These algorithms decide whether a car needs to apply brakes or to take a left turn. The next movement of vehicles depends upon the classification, recognition, and prediction of objects.

Ex: Adaboosting

Pattern Recognition Algorithms

In ADAS, sensor-obtained images contain all types of environmental data. Sorting and filtering of these images are required to classify the objects distinctly. Irrelevant data points have to be removed entirely. These algorithms help in ruling them out. Therefore, these algorithms are also known as data reduction algorithms. Line segments and circular arcs are the most important features of an image. They are used for recognizing an object.

Ex: Support Vector Machines (SVM) 

KEY CHALLENGES

• Interference of lidar signals

• High-frequency range

• Unfavourable weather conditions

• Implementation of traffic rules

• Achieving zero-emission

• Liability for accident

FUTURE PROSPECTS

Autonomous driving is the future of the modern transportation system. Some more aspects of machine learning are yet to be explored. Autonomous vehicles will help to reduce traffic congestion, cut transportation costs and improve walkability. These vehicles are a promise of a new future with the potential of dramatically lowering carbon dioxide emissions.