Shanghai (Gasgoo)- Chinese NEV maker BAIC BJEV and visionICs, a Silicon Valley-based technology company, recently set up a joint laboratory of autonomous driving technology in Silicon Valley to research and develop the solid-state LiDAR-focused next-generation multi-sensor fusion autonomous driving system, according to a Chinese media outlet.
BAIC BJEV revealed that the joint laboratory will conduct in-depth R&D of autonomous driving strategy, LiDAR, multi-sensor fusion system as well as high-level autonomous driving, with the aim of improving the company's technology level and optimizing users’ experience.
As a crucial sensing part for autonomous driving system, LiDAR is able to make the real-time 3-D scanning over surroundings as accurate as one centimeter, boasting apparent superiority over other sensors in accuracy and responding speed. However, it is still hard to largely use LiDAR in mass-produced models limited by the relatively higher cost, power consumption and instability. The next-generation solid-state LiDAR, featuring the same working principle as the phased array radar applied in the J-20 fighter jet, sports advantages over the existing camera- and millimeter-wave radar-centric volume autonomous driving system in performance, cost and reliability.
Focusing on autonomous driving sensing technologies, visionICs reportedly possesses the world-leading solid-state LiDAR chip and AI technologies. Dr. Li Cheng, founder of visionICs, stated that both companies will share technologies with each other based on the newly-built joint laboratory, integrating visionICs's cutting-edge technologies in in-car sensor chip and AI into the EV maker's autonomous driving strategy.
BAIC BJEV opened a R&D center in Silicon Valley as early as 2016 that aims to help it explore and search latest technology resources by fully entering the core R&D industrial ecosystem there. The latest foundation of the joint laboratory signals BAIC BJEV’s ambition to absorbing the most advanced technologies into its products through more abundant approaches.
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