Smart phones. MP3 players.
Notebooks. We can’t live
without our portable gadgets.
However, we probably
drive our most computeintensive
mobile electronics
to work everyday. Today’s
automobiles use a variety of
networks, sensors, and computer platforms to
deliver safer and more pleasant travel than ever.
Most companies concentrate their development
efforts on safety, efficiency, and performance.
These features rank high with consumers,
and the newest and most sophisticated
features always appear first in high-end models
like the Lexus LS 460 (Fig. 1).
As with all modern cars, the LS 460 offers
mandatory passive safety features such as seatbelts
and airbags. It also has a voice-activated,
heads-down-display (HDD) navigation system
that’s standard in many high-end vehicles and
an option in most others. The LS 460 leads the
pack by moving into the active safety realm with
NEC’s IMAPCAR (Image Memory Array Processor
for CAR) image-recognition system.
MOVE TO ACTIVE SAFETY
Passive safety systems are mature technologies
that have less payback for new improvements,
but they continue to be refined. They
target post-crash actions, meaning they activate
after a collision has occurred or is inevitable
(e.g., airbags).
Active safety features address accident
avoidance or pre-crash actions. Advanced antilock
braking, traction control, and vehicle stability
controls also fall into this arena. Traction
control and vehicle stability controls benefit
from improved sensors as well as the significantly
greater computing capabilities that are
available in the latest crop of DSPs and
microcontrollers.
These active systems contribute to
improved safety and performance. But
designers also are addressing new areas
due to improvements and cost reductions
in sensors, such as video cameras,
lasers, and radar detectors. From a driver’s
standpoint, new systems like adaptive
cruise control are an active part of the
driving process. They augment the driver’s
senses and provide limited autonomous
control. In the future, cars will exercise
more autonomous control.
For example, initial cruise-control applications
simply maintained a fixed speed.
Some current systems can maintain a
safe but variable distance from the cars
ahead based on the environment. Even
more advanced systems, like those on
the LS 460, can apply the brakes in anticipation
of a collision. Warnings are being
improved as well, from simple tones to
more complex audio and visual cues.
New systems warn the driver if there’s
a high probability of a collision. If the
driver doesn’t react in time, the system
will employ recommended actions such
as braking. Passive systems also assist
in anticipating the collision and operating
in a more optimal fashion. For instance,
airbags needn’t be deployed when no one
is sitting in their respective seats, or they
can deploy with less force if the occupant
is small. Weight sensors help make these
determinations, but ultrasonic or even
vision systems can be employed, too.
Today’s designs incorporate more
sensors to provide more contextual,
environmental information so computers can become part of the decision loop (Fig.
2). Sensor fusion, or the combination of sensor
information for a typical task, will become
more common. Adaptive cruise control can use
vision and radar sensors to determine where an
obstacle, such as another vehicle, is located.
No one sensor system meets all of the requirements
for current and forthcoming active safety
systems, but vision is definitely one of them.
ON THE HORIZON
Low-cost, high-performance imaging and
computational hardware is bringing vision to
the forefront of automotive safety. So are
improved algorithms and applications for image
recognition and analysis. Yet the availability
of this kind of hardware in
versions suitable for automotive
use will be critical to
their widespread adoption.
Eventually, vision systems will
be required by law, just like
seatbelts and airbags.
Multicore architectures
that have very large numbers
of processing units will
continue to grow. The current
NEC IMAPCAR processor
employs 128 very long
instruction word (VLIW) processing elements
(PEs) (Fig. 3). Each VLIW instruction can control
four logic units in each PE. A 16-bit RISC
control unit provides the coordination for the
IMAPCAR chip (Fig. 4).
The IMAPCAR’s architecture is designed specifically
for video-feedback applications in the
automotive market. It incorporates the video
input and output into the buffering scheme for
real-time annotation.
The system can handle a number of imagerecognition
algorithms at the same time, providing
information to the host microcontroller
as well as to the driver by modifying the video
stream as it passes through the chip. Each
PE contains its own memory for copying and
analyzing the frame buffer as necessary. Error
correction coding (ECC) and parity are used to
improve reliability.
Low power is also critical to this application
space. The IMAPCAR chip draws only 1.7 W
running at 100 MHz, delivering 100 GOPS of
performance. Currently, the IMAPCAR system
can handle lane and pedestrian recognition. The
Lexus system employs two cameras in a stereo
configuration as well as millimeter-wave radar,
delivering features such as lane departure warnings.
A third camera covers rear viewing.
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