The Usage of Machine Learning in Autonomous Vehicles Courses

The development of autonomous cars or vehicles is the yield of the automobile industry. Integrating AI and machine learning with the automobile industry has enabled companies to launch a fleet of fully automated cars. As a result, several companies that teach the understanding of the complete architecture and components are booming. The article talks about the skills required by an automobile engineer who wants to learn about self-driving car specialisation and the working methodology of automated cars. 

How do autonomous cars make a decision?

Self-driving cars identify objects on the road with the help of sensors; they interpret conditions and decide based on their information. While analysing the received information, these cars use object detection and object classification algorithms to identify, classify, and interpret.

To reduce failure and overlap several algorithms, autonomous vehicles use machine learning. Machine learning combines several algorithms to ensure safety by taking note of crossroads and identifying lanes, signs, and other obstacles. 

  1. Cameras: The cameras that detect obstacles are highly reliable as they acquire information from every angle to get the perfect view of the surroundings. Also, some cameras attached to the outer body of the vehicles can view about 120 degrees, some have a narrower vision, and others have a long-distance vision. 
  2. Radar detectors: The radar detectors augment the camera sensors at night or in darker environments. The higher-sensitive radar detectors help augment the visibility of the vehicle. The radar sensors send radio waves to locate moving objects and calculate their speed and location.
  3. Laser detection: Laser technology empowers the 3D visuals of surroundings. Lidar sensors calculate the distance; acquire parameters such as shape and depth of the surroundings. LiDAR technology enables autonomous cars to digitally view the environment by producing a 3D point cloud. This digital representation enables autonomous cars to predict obstacles and operate smoothly in the unpredictable real world.

Key Machine learning algorithms:

Some key machine learning algorithms have scaled massive features and efficiently classified them. Therefore, a professional willing to take a Machine learning course on autonomous vehicle specialisation must look for the key machine learning algorithms in the course.

  • SIFT- SIFT is a key machine learning algorithm that helps in detecting objects and interpret images. For example, for a rectangular sign, the four corners of the object are entered as features, and then the vehicle detects the object as a rectangle using the points. 
  • Adaboost algorithm: Adaboost algorithm works for classifying data. Receiving data boosts autonomous vehicles’ learning process and performance. The algorithm helps classify the classifiers based on their performance and then decide.
  • Texton boost algorithm: The text on boost algorithm helps in recognising objects. They receive data by detecting the object’s shape, the obstacle’s context and other microstructures. This augments the visual data with the help of features.

Therefore when selecting an Autonomous vehicle course, the learner must look for the machine learning algorithms, sensor fusion methods, and other aspects such as functional safety and device reliability. In addition, building programs with python would be added skills.


The sensors in autonomous cars use cameras, radars, and lidars and combine their detection to make a decision as humans or better than humans. These sensors help acquire a clear view of the road or the environment. Additionally, they also help the vehicle to identify the parameters of the vehicle, such as location, speed, and 3D shapes of the obstacle. 

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