On December 14-15, the 3rd LiDAR Tech 2021 was held in Suzhou. More than
30 industry experts were invited to discuss and analyze the most advanced
LiDAR technology around hot topics such as new market trends, technological
breakthroughs, mass-production manufacturing, and multi-sensor integration.
Cognata participated in the conference as one of the supporting enterprises,
shared technology with industry leaders, and demonstrated excellent sensor
According to IHS data, the global LiDAR market sales will rise from 1.2 million
units in 2020 to 3.6 million units in 2026, and the global market scale is
expected to reach 348.4 million US dollars from 81 million US dollars in 2020
to 2026. With the technological innovation and industrial development of
China’s intelligent cars, IHS predicts that the automatic driving vehicle will start
explosive growth around 2025, and half of the roads in 2035 will be self-driving.
As the key sensor to realize automatic driving, lidar will also usher in high-speed
development with continuous technological progress and mature market segments.
At the product exhibition site, Cognata’s DNN based sensor simulation technology
attracted the attention of many industry stakeholders, who were very interested in
how the technology platform could help sensor products quickly improve the
validation time and effect. For a LiDAR manufacturer, when providing a radar
solution for an automobile enterprise, it takes a long time to actually build a whole
vehicle, install LiDAR, and then run a road test. On the simulation platform, it can
be solved in almost a few hours. In the evaluation of the simulation effect, LiDAR
manufacturers will more consider whether the simulated point cloud can replace
the real point cloud.
Cognata autonomous cloud simulation platform supports physical level modeling
and Simulation of a variety of cameras, RADASs, LiDARs, and thermal imagers to
help OEMs, Tier1 ADAS manufacturers test and verify the full stack AV algorithm
(perception algorithm, decision algorithm, and control algorithm), support
distributed deployment in the public cloud and private cloud, accelerate software
iteration, help users deliver the automatic driving scheme in advance, and save a
lot of software and hardware costs.