How 3D laser LiDAR solve the problems of autonomous vehicle’s near-field detection of blind spots?
According to the global status report on road safety from the World Health Organization, approximately 1.35 million people died in traffic accidents in 2018, and 3,700 people died in car accidents every day, that’s to say, around 3,700 people died in car accidents every day. Nevertheless, almost all of these injuries and deaths were caused by human error. As one of the most important future technologies in the 21st century, we believe that self-driving cars will effectively eliminate accidents that are caused by human error.
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RS-Reference 2.1 Can Efficiently Evaluate the Performance of the LiDAR and Multi-Sensor Fusion Sensing System
[Shenzhen, China] — RoboSense LiDAR (https://robosense.ai/) today released the latest version of the ground truth data system and evaluation tool chain RS-Reference 2.1, used for LiDARs and multi-sensor fusion systems performance evaluation. The original RS-Reference version was launched in the market in 2016, when the automotive-grade MEMS solid-state LiDAR RS-LiDAR-M1 project was established. Used by global OEMs and Tier1s, the system has been continuously improved and upgraded with more efficient and useful evaluation function modules and software tool chains.
While the evaluation function modules can…
Point Cloud Quality Is The Key Criteria in Evaluating LiDAR Performance
When talking about 3D LiDAR performance, it’s universally agreed that the parameters of LiDAR are important. Nevertheless, parameters are just the “fundamentals”! When working in complex traffic scenarios, LiDAR for self-driving cars needs to tackle various extreme working conditions comprised of high reflectivity objects, near-field obstacles, strong ambient light (direct sunlight), and interference from other LiDAR sensors, etc., which any of them may cause abnormal to the point cloud that may “fool” the processing algorithms and furthermore lead to tragic accidents. These extreme working conditions are “Corner Case”. …
The Five Key Aspects Of A Mass-Production-Ready, Automotive-Grade, Solid-State LiDAR
From demo to production, different product stages have different requirements for LiDAR’s mechanical design, hardware, software, validation, verification and reliability. For automotive LiDAR, it usually takes several years to move from a concept to a stable mass production product in order to support the complete commercial autonomous vehicles.
After five years’ investments in iterations of five major stages and dozens of small stages, RoboSense has finally announced at the CES2021 and demonstrated the SOP version of the automotive-grade MEMS solid-state LiDAR RS-LiDAR-M1 (referred to as “RoboSense M1”). …
RoboSense LiDAR
The Leading Provider of Smart LiDAR Sensor Systems for Autonomous Vehicles and Robotics
