Autonomous Vehicles - KPIT

Figure 1: A robotic Volkswagen Passat
with cameras and other sensors [3]
II. Cameras and stereo vision
Cameras are the optoelectronic device used
to capture a real 3D scene into a 2D image
plane. While acquiring an image, the
continuous real scene is discretised into pixels
and hence provides information about the
scene in discretised format. Cameras are
widely used in unmanned vehicles to acquire
information about the scene and the captured
data is processed to help self-driven robotic
cars. Cameras can replicate driver's eyes in
autonomous cars. By keeping more cameras,
it can be used to identify the objects in blind
spot region (which is handled by Blind Spot
Monitoring) while changing lane as well as in
parking (which is handled by Automatic
Parking System). Some of the applications of
cameras in autonomous cars include
detecting lanes, obstacles, neighboring
vehicles, and traffic signals. Usage of
cameras provides flexibility for autonomous
vehicles because it can be used in adverse
climate. The images acquired in hazy
environment can be converted to similar
images taken in normal condition using postprocessing
technique called “De-weathering”.
Similarly, there are many post processing
techniques which can convert corrupted
image into an image taken at normal
condition. Some of the post-image processing
techniques are fog, smoke and rain drop
removal, image de-blurring and low light
image enhancement. The ability to postprocess
the image acquired makes the
cameras inevitable for unmanned vehicles. A
low cost scientific camera with 400
frames/second and 1.5 megapixels resolution
is available for 350 USD in the market [12].
Stereo vision is a computer vision technique
which utilises two cameras facing the same
direction to produce 3D view of the real 3D
scene by just acquiring images. This
technique helps to recreate the 3D view of the
scene and facilitate the system to recognise
objects and analysis motions [4]. Hence
computer vision technique along with image
processing techniques are utilised in
o
autonomous car to generate 360 view of the
actual scene. This recreation of 3D view
enables the car to see everything around it
and make decisions about every aspect of
driving. As the cost of cameras is driving down,
m o s t o f c o m p a n i e s i n t e r e s t e d i n
manufacturing autonomous cars are trying to
adopt vision based system for sensing the
surroundings. As mentioned, stereo vision
setup requires two cameras mounted on a flat
plate or fixture and hence it cost depends on
the cameras opted.
III. Laser interferometry detection
and ranging technology (LIDAR)
LIDAR uses spinning lasers and photoelectric
diodes to create a virtual model of its
surrounding. It works by illuminating the scene
with laser and detecting the reflected ray using
photoelectric diode. The time taken by the
laser is determined and used to measure
distance of the object from the laser. The same
principal is followed while using the spinning
lasers and diodes to recreate the 3-D surface
of the scene (see Fig. 2). The resolution of the
reconstructed scene can be improved by
increasing the frequency of spinning lasers
and the number of lasers. The ability of the
laser to reflect back from wide range of objects
is due to high energy and shorter wavelength.
But due to its harmful effects on human eye
and high cost, this technique becomes less
preferable than camera based vision. In
market, LIDAR capable of producing 6000
points per second can be purchased for $6000
USD while 1.3 million data points per second
for $75,000 USD [8].
TechTalk@KPIT, Volume 6, Issue 4, 2013
523