3. Driver safety and accident reduction depend on modern cars' driver support systems.
Lanes are hard to see. It estimates road-vehicle distance. This study introduces an on-
board camera windscreen-viewing vision system. A automobile camera captures the front
view and many methods identify lanes. The Hough transform finds lane boundaries from
two fitted plots. The suggested lane detection method works on straight and curving
highways, painted and unpainted, in various weather situations. The proposed method
does not require lane width, crossing duration, or lane offset. Camera calibration is
unnecessary. The system was tested under diverse lighting, shadow, and road situations
without speed limits. The device accurately detects road lanes.
ABSTRACT
4. •An autonomous car is a vehicle capable of
sensing its environment and operating
without human involvement. A human
passenger is not required to take control of
the vehicle at any time, nor is a human
passenger required to be present in the
vehicle at all. An autonomous car can go
anywhere a traditional car goes and do
everything that an experienced human driver
does.
The basic requirement for self driving cars is
to detect the lanes and keep the cars in
between the lanes.
INTRODUCTION
5. Advantage
There are many advantages of self-Driving
cars.
• Our roads will be safer
• We will be more productive
• We will move more efficiently
6. CANNY EDGE
DETECTOR
implementing
the following 5 steps in order of execution
1) Applying a Gaussian filter for noise removal and
image
smoothening.
2) Computing the intensity gradients for all the pixels in
the
image.
3) Applying a process called “non-maximum
suppression”
to avoid unauthentic response to edge detection.
4) Applying a double-threshold categorization to
evaluate
edges, and determine the potential ones.
5) Evaluating edges by categorization: completing the
detection of edges by removing all the other edges
that are
in the low category or are weak but not associated
(close
to or connected) to edges in the high category.
8. HOUGH
TRANSFORM
Hough transform can be
used to detect straight lines,
circles,
ellipse, and other arbitrary
shapes in images. It finds the
location of lines in an image
as given by equations
The original
image.
Récent Patents
on Computer
Science
9. YOLO ALGORITHM
YOLO algorithm works
using the following
three techniques:
•Residual blocks
•Bounding box
regression
•Intersection Over
Union (IOU)
IMAGE
YOLO
ALGORITHM
YOLO is an algorithm that
uses neural networks to
provide real-time object
detection. This algorithm is
popular because of its
speed and accuracy. It has
been used in various
applications to detect traffic
signals, people, parking
meters, and animals.
STEPS
10. CONCLUSION
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dolorem.
This study introduces "LaneRTD," a fast and accurate lane-line recognition
and tracking method. LaneRTD uses well-known methods like Canny edge
detection and Hough transform. The pipeline also detects and draws lane
lines to produce the final result. The proposed method requires only raw RGB
images from a single CCD camera situated behind the vehicle's windscreen.
Many stationary photos and real-time movies are used to test the LaneRTD.
Except for one situation with complex shadow patterns, the validation results
are accurate and robust. The LaneRTD's low overhead and high throughput
(execution time) made it ideal for real-time lane detection. Thus, the proposed
method is suitable for Advanced Driving Assistance Systems (ADAS) or self-
driving cars.
11. CURRENT & FUTURE
DEVELOPMENTS
A comprehensive discussion and analysis
regarding the usefulness and the
shortcomings of the proposed technique
as well as suggestions for improvements
and future work are presented.
