Signal Processing - March 2017 - 121

in the spotlight

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can have an impact. Our algorithms
and our technical areas are precisely
the tools that are needed to advance
vehicular systems. We have been conducting research in a broad range of
fields, such as radar, computer vision,
and statistical signal processing that
can allow us to revolutionize the field,
to develop the necessary innovations
that will make automotive systems reliable to the tenth decimal place. Here we
take a look at this problem and present
four promising ways signal processing
and data analytics can have an impact
on the challenges surrounding automated vehicles.

Properly assess and utilize data
for safety decisions
Cars currently contain roughly 100 sensors, and future automobiles will likely
be deployed with significantly more, including accelerometers (for impact detection and motion measurements),
pressure sensors (for air intake control,
monitoring fuel consumption, tire
conditions), temperature sensors (to
monitor and control engine conditions, fuel temperature, passenger
compartment temperature), and
phase sensors (camshaft/crankshaft
phase sensors for motor control, and
gear shaft speed for transmission
control). Angular rate sensors monitor
the roll, pitch, and yaw of a vehicle,
which informs dynamic control systems, automatic distance control, and
navigation systems. Angular and position sensors monitor the position of
gear levers, steering wheel angle, and
mirror positioning. Radar, lidar, and
camera sensors are used to facilitate
new applications, such as blind spot
monitoring, lane-departure warning,
and automated driving.
These sensors provide the abundance of data that can serve as corroborating evidence to fix malfunctions,
back-solve and determine a mistake is
about to be made from using a single
sensor type alone, and correct false data
injected by those trying to hack our
vehicles. When properly utilized, this

wealth of data is the avenue to safety
and robustness.

Merge multiple types of imaging
sensors for fast object recognition
It's evident that the Tesla crash videos
recorded by Autopilot weren't under
ideal lighting conditions. Background
objects blended into vehicles that needed to be recognized, making it difficult
for any computer to process correctly.
This was amplified by the short time
allowed to "lock on" given the speed
of the vehicle and the imminent crash.
Multiple sensor types used in conjunction could have helped. Radar or lidar
would not have been susceptible to
the same difficulties the camera-based
system likely encountered.
Tesla has since reevaluated its strategy for Autopilot, including the possibility of using radar in place of the camera,
and two things are clear: the choice of
a radar system is meant to avoid the
environmental hurdles that arise with
visual-based systems, and Tesla has
collected a large amount of radar data
that serves as the basis of its new Autopilot system.
Though likely an improvement,
switching to a single type of sensor isn't
likely to solve all the problems that
will arise in automating vehicles. In fact,
while radar can cope with lightingbased challenges, numerous studies suggest lidar systems are superior in terms
of tracking accuracy. While lidar systems suffer degradation in conditions
with fog, cameras offer the ability to
recognize finer details associated with
objects (such as license plate information). In fact, cameras support the accurate assessment of the visibility
distance (notably fog), which could
be used to inform the driver that vehicular assistance services aren't
available or are experiencing degraded quality of service because fog is
affecting the visibility of road lanes
and other vehicles. Data fusion and
extracting hidden correlation between
sensor types is at the heart of modern
signal processing. Merging radar,
IEEE Signal Processing Magazine

March 2017

lidar, and visual systems into fast and
robust object recognition and tracking
algorithms is an exciting opportunity
where signal processors can contribute.

Share data between vehicles
to correct miscalculations or
other errors
When considering future vehicular
applications, we should recognize
other sensor types will be available
and can provide valuable knowledge,
like weather conditions, road friction
coefficients, or road slopes. Road slope
information is useful for coordinating
braking among several vehicles since
slope is related to the potential for a
vehicle to accelerate or decelerate.
Data sharing between vehicles and
cloud-based computing services
opens up many other possibilities to
improve vehicle safety. Data shared
between vehicles will allow signal
processing algorithms running on
each vehicle to gather the conditions
that may be experienced by other
nearby vehicles.
Furthermore, data measured by the
multitude of sensors, whether from
within a single vehicle or across several, can be used to correct malfunctioning or poorly calibrated sensors.
Currently, vehicular sensors are recalibrated by bringing a vehicle to a certified garage to update or replace the
sensor. By using the distributed nature
of the vehicular setting-in which there
are numerous vehicles frequently making data measurements correlated
across many dimensions-it becomes
possible to report this data to cloud
servers that would perform large-scale
data analytics to accurately identify the
corrections needed.

Understand human driving
behavior through signal
processing advancements
Going beyond the technical aspects,
what is forgotten is that transportation
also serves as a complex social fabric
by which we interact with each other.
This merging of "cyberphysical" with
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Table of Contents for the Digital Edition of Signal Processing - March 2017