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About this session
From a self driving car’s perspective, the closest objects in its direction of motion are far more important than the farther objects. And depending upon the dynamics of other objects around the car, less important objects may become very important in just a few milliseconds. Conventional LiDARs use a fixed scanning pattern to sense surroundings and create a high resolution, 3D point cloud. The fixed scanning pattern tries to strike a balance between the ‘3 Rs’ – range, resolution and refresh rate, but fails considerably. Although resolution is high, it comes at the expense of range and refresh rate. Agile LiDAR that is situationally adaptive so that it can modify scan patterns and trade resources such as power, update rate, resolution, and range, enables the creation of intelligent sensing with a fourth R – ROI (Region of Interest) and the ability to foveate on one or more objects depending on the scene dynamics. This advancement in LiDAR is crucial for creating perception-optimized scan patterns and increasing the accuracy and speed of perception algorithms.