This document discusses self-driving cars and machine learning algorithms. It describes how self-driving cars can navigate without human input using sensors to perceive the environment. The document outlines that machine learning algorithms like regression, pattern recognition, clustering, and decision matrices are used to classify objects, predict events, and determine navigation. Both advantages like safety and disadvantages like limitations and hacking risks of autonomous vehicles are presented.

1. SELF DRIVING CAR
NAME: KAVAN PRAJAPATI
BATCH: A
ENROLLMENT NUMBER: 9191051003

2. OUTLINE
 What is it ?
 Machine learning algorithm for self driving cars
 How it works ?
 Advantages and disadvantages
 Self-driving cars vs others
 Conclusion
 Video
 References

3. WHAT IS IT?
 Ability of a car to get from point A to point B
without human interaction(without driver).
 As an autonomous vehicle, it is capable of sensing
its environment and navigating without human
input.

4. MACHINE LEARNING ALGORITHMS FOR SELF
DRIVING CARS
 Self-driving car Machine Learning algorithms are generally divided into four categories:
1. Regression Algorithms
Regression algorithms are used explicitly for predicting events. Bayesian regression, neural network regression,
and decision forest regression are the three main types of regression algorithms used in self-driving
cars.
2. Pattern Recognition Algorithms (Classification)
These algorithms help in filtering the data obtained through the sensors by detecting object edges, and fitting
line segments and circular arcs to the edges. Pattern recognition algorithms combine the line segments and
circular arcs in many different ways to form the ultimate features for recognizing an object.

5. 3. Cluster Algorithms
K-means and multi-class neural networks are the two most widely used clustering algorithms for
autonomous cars.
4. Decision Matrix Algorithms
These algorithms determine the moves of the self-driving car. So, whether the car needs to take a left or a right
turn, whether it needs to brake or accelerate, the answer to such questions is determined by the accuracy of
these algorithms concerning classification, recognition, and prediction of the objects’ next movement.

6. HOW IT WORKS ?

7. ADVANTAGES
 Time-saving
 No occupant restrictions :
1. Drunk
2. Sleepy
3. Disabled, under age, or over age.
 Stress-free parking
 React faster than humans Fewer traffic collisions.

8. DISADVANTAGES
 Speed in this car is limited
 Expensive
 Fewer job opportunities for others
 Prone to Hacking
 Failure of just one system in the car could be very dangerous.

9. SELF-DRIVING CARS VS OTHERS

10. CONCLUSION
 Together, all these ML algorithms go into the functioning of self-driving cars as we know it. At present, self-
driving cars can perform the basic tasks of a human driver, such as controlling, navigating, and driving the
vehicle, but of course, there are certain limitations to it as well. However, with further advancement of
Machine Learning and improvement of self-driving car algorithms, we have a lot to look forward to from
these autonomous cars.

11. VIDEO !!
 https://www.youtube.com/watch?v=xMH8dk9b3yA
 https://www.youtube.com/watch?v=2yCl9WE4mzw
 https://www.youtube.com/watch?v=Ly92UcnoEMY

12. REFERENCES
 https://robohub.org/how-do-self-driving-cars-work/
 https://en.wikipedia.org/wiki/Self-driving_car
 http://www.halffastchicago.com/advantages-and-disadvantages-of-autonomous-car/
 https://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works