The
evolution of the automobile is quite interesting to understand. In 1700 we had
a Steam and Electricity powered car. Later in the 1800s, we had the first
patented gas-powered car. There was no looking back since then. From a car with
no windshields and steering wheel, we now have a driverless car!
Introduction
of Self-Driving Vehicles
The
idea of the self-driving vehicle was introduced by General Motors in the year 1939. It
was a radio-controlled electric vehicle. The self-driving car has undergone a
complete transformation since then and it is an autonomous vehicle. An
autonomous vehicle uses a combination of sensors, artificial intelligence,
radars, and cameras to operate, without any human intervention. This type of
vehicle is still in its development stage since there are various components to
be considered to make it safe for its passengers.
Computer
Vision Technology
The
inclusion of Computer Vision technology can make the
autonomous vehicle safe for its passengers as per the researchers and
professionals. There are various research carried on to check its viability.
Computer Vision is the core of Artificial Intelligence technology. AI helps
computers to decode and understand the visual data acquired from various
sources. It involves automatic visual understanding using AI algorithms. The
best example of computer vision is the facial recognition tool. In autonomous
vehicles, it is used with sensor technology to identify people, cars, and other
objects on the road.
How
has Computer Vision Technology made the Autonomous Vehicle reliable &
intelligent?
Recognize
Objects
There
are various moving as well as stationary objects on the road like pedestrians,
other vehicles, traffic lights, and more. To avoid accidents or collisions
while driving, the vehicle needs to identify various objects. Autonomous
vehicles use sensors and cameras to collect data and make 3D maps. This helps
to identify and detect objects on the road while driving and makes it safe for
its passengers.
3D
Map Creation
The
cameras on the vehicle can capture images in real-time. Computer vision uses
real-time images to create a 3D map. With the usage of 3D maps, autonomous
vehicles can decipher the driving space for risk-free driving and also opt for
alternate routes in case of projected collision. This makes driving easy and
accident-free for its passengers.
Deployment
of Airbags
The
data of surroundings are continuously decoded by computer vision. It can
predict any collision or probable accidents in advance. It can deploy airbags
well in advance to protect its passengers in case of an unavoidable collision.
The safety of the passengers is the top priority and computer vision is
intelligent enough to ensure the same.
Tracking
of Cars
Computer
vision uses bounding box detention with complex algorithms to detect if the car
on the road is the same as before. This is useful to track and also predict
behavioral patterns of other drivers and to make driving safe.
Lane
line Detection
Cutting
lanes can prove to be a disaster in the case of self-driving vehicles. Computer
vision with Deep learning technology uses segmentation techniques to detect
lane lines and to stay in the stipulated lane while self-driving. It can also
detect the curves and turns on the road making it a safe experience for its
passengers.
Low
light mode driving
The
light condition will differ as per the route, terrain, and time of the day. Self-driving
vehicles need to switch between normal and low light modes. The images captured
in the low light condition are often blurry and make driving difficult and
unsafe. Computer vision with its algorithms can identify the low light
condition and adjust to the same while driving. This is done using LIDAR &
HDR sensors, FMCW Radars, SAR and thermal cameras.
Data
for Training
The
self-driving cars to ensure safe driving condition captures data about the
location, road & traffic conditions, terrains, number of people in the
areas and more. These data sets are used for situational awareness while
driving. The same data sets are useful for deep learning model training. For
instance, the images of traffic signals at various junctions captured by the
camera are used by computer vision to check traffic signals while training deep
learning models. It also helps to identify and classify various types of
objects on the road.
Conclusion
Computer
vision with an AI-based algorithm is the “eye” of self-driving vehicles. The
main objective of computer vision is to ensure the safety of its passengers and
to deliver a smooth self-driving experience. The technology hasn’t been
perfected yet as few limitations need to be fixed. But the pace at which the
technology is progressing, intelligent and reliable self-driving car using
computer vision will soon be seen on the roads.
Aventior delivers top-class
computer vision services not only to the automotive industry but also to other
domains like healthcare, life sciences, aerospace, and manufacturing.
Aventior’s team of highly skilled computer vision engineers are engaged in
projects belonging to various domains including life science, healthcare,
automotive, energy, manufacturing, and aerospace. They ensure to meet the
product goals efficiently and create the most competitive products for clients.
Furthermore, Aventior has recently launched its Vehicle Detection Deep Learning Algorithm on the ESRI marketplace. This Deep Learning based process block is capable of
detecting and counting various vehicles. To know more about our services, write to us
at info@aventior.com.
