NetworkNewsWire
Editorial Coverage: Self-driving cars are already appearing on
our roads. One of the main technological barrier holding them back
from full use is the creation of effective sensor systems, and
several companies are conducting specialist research in this area.
Foresight Autonomous Holdings Ltd. (NASDAQ: FRSX) (TASE:
FRSX) (FRSX
Profile) has created a unique system
that combines infrared and visible light cameras in stereo
technology that can detect obstacles under all weather and lighting
conditions. Google’s parent company, Alphabet, Inc.
(NASDAQ: GOOG), is developing driverless cars through its
Waymo subsidiary, using a wide range of different sensors. The work
of Tesla, Inc. (NASDAQ: TSLA) in this area is
well-known and heavily reliant on a range of visible light cameras.
Automotive safety specialist Autoliv, Inc. (NYSE:
ALV) has created a range of separate detection systems
using different technologies. Apple, Inc. (NASDAQ:
AAPL), on the other hand, is focusing on the potential of
a single complex lidar system. It’s a diversity of approaches that
shows a technology approaching maturity.
The Future of Driving
Technology commentators are predicting big things for
self-driving cars. These autonomous automobiles are not just
expected to save car users from the effort of driving. By making
the most of efficient computing and by removing human error, these
cars have the potential to improve the flow of traffic, reduce fuel
usage and increase mobility for those who can’t drive themselves,
such as the elderly and disabled. Despite alarmed responses to the
idea of not having a human behind the wheel, self-driving cars are
also expected to increase road safety and reduce accidents.
All of this — especially the reduction of accidents — is reliant
upon the development of effective systems for the vehicles to sense
what is going on around them and respond appropriately. Both the
sensors and the processors dealing with this input are vital to
making autonomous cars safe and effective. Radar and lidar have
drawn the most attention, thanks to advances in these areas.
Camera-based vision sensors have also seen significant
advances.
New Detection Technology
Such a crucial area of technology needs specialist research and
design to ensure that the best solutions are found. Foresight Autonomous Holdings (NASDAQ: FRSX)
(TASE: FRSX) is a company focused on this specialism.
Working through wholly owned subsidiary Foresight Automotive Ltd.,
Foresight is designing, developing and commercializing a range of
technologies around detection systems for automated cars. These
include stereo/quad-camera vision systems based on 3D video
analysis, advanced algorithms for image processing and sensor
fusion.
- The company’s leading product is its QuadSight detection
system. This stereoscopic automotive vision system uses two sets of
stereo cameras — one infrared and the other working with visible
light — to detect any obstacles on the road. It can detect
obstacles regardless of adverse weather or extreme lighting
conditions, making it a highly reliable option for self-driving
cars regardless of the circumstances. It detects all obstacle,
regardless of shape, form or material and color with near zero
false alerts, which are the downside of highly sensitive detection
equipment.
“At Foresight, we believe that a car’s vision system should be
nothing less than perfect,” said Haim Siboni, the company’s CEO.
“Vision is the foundation of passenger safety, and vision
perfection under all weather and lighting conditions is clearly the
breakthrough that vehicle makers need to build consumer confidence
in order to accelerate autonomous vehicle adoption.”
Founded in 2015, Foresight has already completed a feasibility
study for the QuadSight system, carried out extensive testing, and
developed and produced a demo version. The company is creating a
prototype for pilot projects so that the system can be tested out
on the roads. It expects to see that system completed and
commercialized during the second half of next year.
The first quad-camera multi-spectral vision solution of its
kind, QuadSight uses advanced and proven image-processing
algorithms and is derived from its major shareholder Magna B.S.P’s
field-proven Homeland Security vision technology that has been
deployed worldwide for almost two decades and is IP-protected by
patents. With a fully developed system ready for demonstrations,
2018 is the year that QuadSight goes out into the world. So far,
the company has done so with style.
A Strong Showing at CES
The QuadSight system drew a lot of positive press for Foresight
during the International Consumer Electronics Show (CES) 2018.
Given the focus on self-driving cars in recent years, a lot of
public and press attention was on what detection systems could
bring to the autonomous vehicle game, and QuadSight’s unique
features caught people’s eyes.
Electronic Design presented an article that went into detail on
the Foresight system (http://nnw.fm/ym4Us). The article discussed the range
of the detection system and the fact that it can detect details
better than the human eye, with the detection of small objects
allowing it to operate at high speeds. The site also covered the
key technical difference between QuadSight and many of its
potential competitors — the fact that it uses a passive system that
processes all the visual information already available in the world
around it rather than having to send out signals as lidar and radar
do.
Automotive World highlighted the cost benefits of Foresight’s
system (http://nnw.fm/wT5F4). Using multiple sensory
technologies increases the cost of a self-driving vehicle, both
through the sensors themselves and through the processors needed to
deal with the information they provide. QuadSight provides a
complete detection system based on purely visual inputs that could
eliminate the need for complementary sensors and their processing
support.
For EE Times, the focus was on the unique combination of
infrared and visible spectrum cameras (http://nnw.fm/cf6RI). The fusion of these two
technologies allows QuadSight to detect obstacles both day and
night and at any weather condition. They also combine to achieve
both ranging and imaging, allowing the car detect how far away the
object is without any need for additional sensors.
The Pattern Recognition Problem
The way that QuadSight uses its sensory data may give it another
advantage compared with leading competitors. Some self-driving
initiatives rely on pattern recognition as a means of detection and
to help the car judge whether or not there is a hazard. This is
believed to be the technology used in Tesla’s efforts to create
autonomous vehicles. It relies on the system recognizing the form
of an object as a mean of detection and then using this information
to judge how to react. If this is true, then the
pattern-recognition technology may be behind the crashes (http://nnw.fm/w2gqD) that have brought unwelcome
attention to Tesla’s on-road testing.
QuadSight does not use pattern recognition as a mean of
detection but uses unique algorithms to detect any obstacle
regardless of shape, form, material or color. It’s a technology
that gives the system an advantage in responding to unexpected
events — one that might have detected the fire truck involved in
the most recent Tesla crash this month.
Finding Solutions for Self-Driving Sensors
A number of companies are working on sensor technology for
automated cars, whether in isolation or as part of developing whole
vehicles.
Alphabet, Inc. (NASDAQ: GOOG), the parent
company of Google, is one of the leading players in the creation of
driverless cars through its Waymo subsidiary. Its vehicles detect
objects through a wide range of technologies, including sonar,
stereo cameras, lasers, lidar and radar. These systems serve
different purposes, from generating a map of the vehicle’s
surroundings to identifying the presence of other vehicles and
judging the speed at which they are moving. It’s by bringing these
data points together that the system can judge what is going
on.
One of the great modern tech innovators, Tesla, Inc.
(NASDAQ: TSLA), is famous for its work in developing
autonomous cars. Cameras play a big part in Tesla’s detection
technology. These are always mono-visible light cameras, so the
system doesn’t have the ability to see in conditions where only
infrared sensors can detect objects. Recent experiments with
trifocal mono cameras are expanding the system’s detection capacity
by considering views at varying distances.
Autoliv, Inc. (NYSE: ALV), the world’s largest
automotive safety supplier, has developed a wide range of detection
systems designed as additions to the information available to a
driver, as well as options for increasingly automated cars. Its
technology includes radar, lidar and a variety of camera
technologies such as mono vision, stereo vision and infrared. This
range of sensors provides car manufacturers with a variety of
options to detect hazards on the road. Its various styles of camera
currently exist as separate solutions, not an integrated system
bringing their data together.
Starting in 2014, Apple, Inc. (NASDAQ: AAPL)
began work on producing an electric car. This project has since
been scaled back to the creation of autonomous driving systems that
could be applied to other manufacturers’ cars. The company has been
tight-lipped about its efforts, but revealed last year that it is
working with a lidar-only system (http://nnw.fm/lzyL4). Some have argued that lidar
alone can’t provide sufficient information, but Apple aims to use
complex computing and artificial intelligence to make a complete
lidar-based solution.
As various companies race to develop self-driving cars, their
sensor systems will be vital. A company whose system can operate
safely in all conditions, without the extra costs of multiple
sensor types of massive processing, will have an edge in dominating
this important market.
For more information about Foresight Autonomous Holdings, visit
Foresight Autonomous Holdings (NASDAQ: FRSX)
(TASE: FRSX).
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