E-mobility Technology Winter 2020

e-mobility Technology International | Vol 7 | Winter 2020
Classification and Analysis Function Training
For object classification, the Analysis function is
based on neural networks that require training.
Known objects tagged in images are used to
teach the AI the ability to classify objects. LiDARs
can provide the distance of detections and help
build a three-dimensional map that facilitates
the data sorting for classification.
Figure 7 shows classification results using the raw
data from a LeddarTM Pixell LiDAR. The resolution
of a few pixels per object is sufficient for correct
identification.
AI Learning can start with
simulations, but the final training
needs to come from real-life
situations. The next step towards
the final implementation is
prototyping and data collection on
a moving vehicle. Figure 8 shows
a quick and efficient prototype for
data collection before the full AD
EV integration is completed.
Figure 7 – Pedestrian classification using LeddarPixell and AI,
with full waveform raw data
Figure 8 – From prototype to full integration
Conclusion
Automated-driving Electric Vehicles are the short-,
medium-, and long-term future of transportation.
Emerging applications are being developed to
facilitate services such as public transport, goods
delivery, and other specialized applications. The
ability to position and classify objects in the
vehicle surroundings is key to path prediction
and increased safety. AD uses sensors and LiDAR,
as one of the sensors, provides intrinsic distance
measurement and offer sufficient angular resolution
for object detection and classification. The current
successful demonstrations of AD with EVs are critical
steppingstones opening mass volume markets, and
LiDARs are a critical part of the solution.
Robert Baribault, Ph.D.
Principal Systems Architect, LeddarTech
e-mobility Technology International | www.e-motec.net
15