Automotive Engineering - December 2024 - 18

INTEGRATING SENSOR DATA:
Selecting an ADAS decision-making process
Exactly when sensor fusion occurs
in ADAS operations, late or early,
impacts the entire system.
The Volvo EX90 uses Luminar
by Jonathon Ramsey
G
overnments have been studying Advanced Driver
Assistance Systems (ADAS) since at least the late 1980s.
Europe's Generic Intelligent Driver Support initiative ran
from 1989 to 1992 and aimed " to determine the requirements
and design standards for a class of intelligent driver support
systems which will conform with the information requirements and
performance capabilities of the individual drivers. "
Automakers have spent the past 30 years rolling out such systems
to the buying public. Toyota and Mitsubishi started offering radarbased
cruise control to Japanese drivers in the mid-1990s. MercedesBenz
took the technology global with its Distronic adaptive cruise
control in the 1998 S-Class. Cadillac followed that two years later
with FLIR-based night vision on the 2000 Deville DTS. And in 2003,
Toyota launched an automated parallel parking technology called
Intelligent Parking Assist on the Prius.
Those were à la carte options with discrete functions. That
Distronic cruise control, for instance, was a radar unit in the S-Class
grille plugged into an ECU in the passenger footwell, which connected
to the sedan's CAN-C Bus. As an SAE Level 1 convenience system,
it could only accelerate and brake within limited parameters.
Today's ADAS are networked suites of data collection devices able
to control a vehicle without driver input. Mercedes' hands-free Drive
Pilot system in today's S-Class counts one lidar sensor, one stereo
camera, four monovision cameras, five radar units, eight ultrasonic
sensors, and a road moisture sensor. On top of that, and common to
all ADAS, there will be an antenna array for positioning data, a
telematics module for communication with the cloud, and a processing
module often called the " compute " where sensor data is turned
into vehicle decisions.
18 December 2024
lidar sensors to generate the point
cloud the ADAS suite uses as it
makes safety decisions.
The fractal complexity problem
A simple flow chart for ADAS order of operations
starts with sensor data acquisition, followed by an initial
object detection phase called perception, enabled
by AI and applied to raw sensor data. Then comes fusion
of the aggregate perception data, which is run
through another AI model that validates the environment
and plans for vehicle action. It concludes with
the execution of a vehicle command.
The vital step in that decision-making process is sensor
fusion, combining perception data into an environment
of accurately identified objects that the vehicle
can safely navigate. ADAS is only as good as its sensor
fusion performance, which boils down to the capability
of the artificial intelligence models used to identify objects
and deal with them. With AI still in its infancy, the
science of sensor fusion is also in its early stages.
" Sketching out the pieces of an early sensor fusion
model is a known process, " a Rivian spokesperson
told SAE Media. " The challenge is that each of those
pieces is fractally complex and requires high reliability
and scale. Continuing to chase down those subsequent
nine-tenths of reliability is orders of magnitude
more difficult. "
A single camera offers various tunable parameters
that hint as this fractal complexity: image resolution in
megapixels, focal length, field-of-view, frame rate, and
dynamic range. Each decision alters the cost/benefit
AUTOMOTIVE ENGINEERING
VOLVO
Automotive Engineering - December 2024

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