Machine Learning in Driveless Vehicles

The automation industry has grown by leaps and bounds in the 21st century. Automobiles have gone from electric to self-driving, raising customer expectations. This has been possible because of breakthrough technologies like data science, machine learning, and more. Data is based on facts and can provide real insights as to how goals can be achieved in a set time. Thus, every industry can go from 0 to 100 swiftly using data as the fuel for the operation.


Driverless vehicles can face many situations on the road. The cars need to be ready for craziest of circumstances for the passengers to entrust their lives. As a machine, it is expected to perform better than a human driver would. The vehicle can’t be programmed for all real-life scenarios, and it has to learn and adapt to all real-world situations. Machine learning provides this capability to vehicles increasing safety and trust. Let’s take a closer look at most popular machine learning algorithms and how they help in facilitating the process of automation to such an extent that machine learning experts are now thinking about using driverless vehicles on roads.


Learning of the Blog


  • What can Machine learning do?
  • Machine Learning Algorithms
    • Scale Invariant Feature Transform
    • AdaBoost
    • Histogram of Oriented gradients Algorithm
  • Final Word


The evaluation of driver condition and classification through data fusion with the help of internal and external sensors is a part of machine learning applications. If these terms are new to you, it is recommended to go through machine learning for beginners.


What can Machine learning do?


Machine learning- a subset of artificial intelligence focuses on improving the performance of a machine. Learning goes beyond the training of data. A computer equipped with machine learning algorithms can form knowledge structures by applying induction, learning from past data processing, and applying it to a new setting. Artificial Intelligence and Machine learning succeed in areas where traditional programming fails. It can be broken down into two categories, namely- Supervised and Unsupervised learning. In brief, supervised learning interprets data and makes predictions based on correct output data. Whereas, unsupervised learning recognizes the inherent structure based on input data only. Deep learning- a class of machine learning is widely used in driverless technology. It uses real-time data to help the vehicles turn raw, untamed complex data into actionable information using—feature learning. 


Driverless cars need numerous sensors that help them make sense of their surroundings, such as radar, GPS, LiDar, sonar, inertial measurement units, and odometry. The ML-based applications make predictions using the sensory information received by the vehicle’s infotainment system. The architecture of these driverless vehicles requires machine vision, which is provided by deep learning techniques. These are the machine learning algorithms that use neural networks whose formulation is, in turn, inspired by the human brain. These algorithms also integrate speech recognition, driver’s gesture, and language translation in the vehicle’s system. The task of the machine learning algorithms continuously render the surrounding environment and forecast the possible changes. The tasks can be classified into four subtasks:


  1. Object identification/recognition
  2. Object detection
  3. Movement prediction
  4. Object localization


Machine Learning algorithms


In driverless vehicles, machine learning algorithms are used for sensor fusion and scene comprehension, localization in space and mapping, evaluating driver’s behavior patterns, and state of mind. Some of these advanced algorithms are covered as a part of the. Here, we look at the vital algorithms that are being used:



  • Scale-invariant feature transform 


Scale-invariant feature transform or SIFT allows object recognition for partially visible objects and image matching. An image database is used by the algorithm to extract salient points of an object. A vehicle using SIFT can identify a damaged road sign based on its inherent features, i.e., features that don’t change with rotation, scaling, noise, or clutter. The algorithm carries out a comparison of every new picture with those extracted from the database to detect correspondence based on critical points. 



  • AdaBoost


It is a decision matrix algorithm that ensures the adaptive boosting of learners. It checks how the performance of other regression and classification algorithms correspond to successful predictions. It facilitates the adoption and combination of multiple algorithms to work together and complement each other. The combined performance contributes to better learning. In all, AdaBoost obtains one robust classifier using many weak classifiers. It allows object detection and accurate decision-making, especially for the face, vehicles, and pedestrians. AdaBoost overcomes overfitting and is often sensitive to outliers and noisy data. It uses multiple iterations to create a powerful composite learner. The weights are adjusted whenever the entity is appended. The classifier calls the boosting part iteratively, and it changes the weights of misclassified examples after each classification step. 

  • Histogram of Oriented Gradients Algorithm


The histogram of oriented gradients or HOG algorithms is the foundation of driverless vehicles and computer vision. It is one of the most basic ML algorithms used in the analysis of a particular area of an image described as a cell. It can analyze the dynamics of the changes in the picture, checking how and in what direction the intensity of the image changes. It creates purposeful image gradients, which are compressed versions of the original image. It is a part of the initial image recognition process used in driverless vehicles. 


Final Word


Machine learning and driverless vehicles are a perfect match which is defining the future of the transportation industry. Decision making, autonomous navigation, driver’s state recognition, and perception most commonly require machine learning algorithms. At present, we are using automation in controlling and shipping, but there is plenty of scope for fully autonomous vehicles in the future. If you are interested in a career in machine learning and curious to learn about driverless cars, check out the best machine learning certification