Soft computing techniques like fuzzy logic, neural networks, and probabilistic reasoning are well-suited for complex problems where information is incomplete or behavior is not fully known. Soft computing has opportunities in intelligent transportation systems through applications like autonomous vehicle control, traffic congestion prediction, vehicle routing, and vehicular ad-hoc networks. However, soft computing approaches for transportation also face challenges of noisy or incomplete data, and ensuring safety and reliability in autonomous systems.

1. Soft Computing:
Opportunities &
Challenges

2. Shah Alam, Selangor, Malaysia

3. Universiti Teknologi MARA

4. Contents
Computing & Problem
Solving
Evolution of Artificial
Intelligence
Overview of Soft
Computing
Opportunities and
Challenges

5. Computing & Problem Solving

6. Computing Tools
Latin word "digit" is used to refer to a
finger (or toe) as well as a numerical
quantity.
word "calculate" was derived from the Latin word for
pebble: calculus.

7. Computing Tools

8. Computer

9. M. Dahiya, Applications of Soft Computing in Various Areas, International Journal of Engineering Sciences
& Research Technology, 2017

10. Hard Computing
Traditional computing techniques based
on principles of precision, certainty and
inflexibility

11. Limitation of Hard Computing

12. Soft Computing

13. Advantages of
Soft Computing
Soft Computing techniques are widely
popular as they are integrated techniques
and highly suitable to find solutions for the
problems which are highly complex, ill
defined and difficult to model.
In general, soft computing is a good option
for complex systems where:
the required
information is
not available
the behavior is
not completely
known
the existence of
measure of
variable is noisy

15. Overview of Soft Computing

16. History

17. Definition

18. Guiding Principle
The role model for soft computing is the
human mind

20. Evolutionary Computation (EC)

21. Fuzzy Logic
(FL)
FL is multi-valued logic introduced by L. Zadeh
in 1965 which allows intermediate values to be
defined between the two conventional
evaluations.
Fuzzy systems are based on Fuzzy Logic; a
generalization of conventional (Boolean) logic
that has been extended to handle the concept
of partial truth - truth values between
“completely true” and “completely false”.
It also provides an inference mechanism that
enables approximate human reasoning
capabilities to be applied to knowledge-based
systems

22. Probabilistic Reasoning (PR)

23. Neural network (NN)

24. Soft Computing is a Hybrid
Method

26. Opportunities

27. Intelligent Transportation Systems (ITS)
modes of transport traffic management
transport networks

28. Intelligent Transportation Technologies
car navigation traffic
signal automatic number plate
recognition speed cameras CCTV
parking
guidance and information weather information

29. Types of ITS

30. Types of ITS

31. Types of ITS

32. Types of ITS

33. Soft Computing in Intelligent
Transportation Systems (ITS)

34. Soft Computing in Intelligent
Transportation Systems (ITS)

35. Soft Computing in Autonomous Driving

36. Fuzzy Logic in Autonomous Vehicles

37. Metaheuristics in Autonomous Vehicles

38. Metaheuristics in Autonomous Vehicles

39. Neural Network in Autonomous Vehicles

40. Neural Network in Autonomous Vehicles

41. Soft Computing in Traffic Congestion
Prediction

42. Neural Networks in Traffic Congestion
Prediction

43. Probabilistic Reasoning in Traffic
Congestion Prediction

44. Fuzzy Systems in Traffic Congestion
Prediction

45. Soft Computing in Vehicle Routing
Problems (VRP)

46. Nature-inspired Metaheuristics for
Routing Problems

47. Nature-inspired Metaheuristics for
Routing Problems

48. Soft Computing in Vehicular Adhoc
Networks (VANETs)

49. Soft Computing in Vehicular Adhoc
Networks (VANETs)

50. VANETs - Applications

51. VANETs - application

52. Metaheuristics in VANETs

53. Fuzzy sets in VANETs

54. Neural Networks in VANETs

55. Challenges

56. Conclusion

57. Main Reference

58. Thank
You
58