Rethinking the Three “Rs”of LiDAR: Rate, Resolution and Range
Extending Conventional LiDAR Metrics to Better Evaluate Advanced Sensor Systems
As the autonomous vehicle market matures, sensor and perception engineers have become increasingly sophisticated in how they evaluate system efficiency, reliability, and performance. Many industry leaders have recognized that conventional metrics for LiDAR data collection (such as frame rate, full frame resolution, and detection range) no longer adequately measure the effectiveness of sensors to solve real-world use cases that underlie autonomous driving.
First generation LiDAR sensors passively search a scene and detect objects using background patterns that are fixed in both time (no ability to enhance with a faster revisit) and in space (no ability to apply extra resolution to high interest areas like the road surface or pedestrians). A new class of advanced solid-state LiDAR sensors enable intelligent information capture that expands their capabilities—moving from “passive search” or detection of objects to “active search,” and in many cases, to the actual acquisition of classification attributes of objects in real time.
Because early generation LiDARs use fixed raster scans, the industry adopted very simplistic performance metrics that don’t capture all the nuances of the sensor requirements needed to enable AVs. In response, many industry leaders, including AEye, are proposing the consideration of three new corresponding metrics for extending LiDAR evaluation. Specifically: extending the metric of frame rate to include object revisit rate; extending the metric of resolution to capture instantaneous resolution; and extending detection range to reflect the more critically important object classification range.
We are proposing that these new metrics be used in conjunction with existing measurements of basic camera, radar, and passive LiDAR performance. These extended metrics measure a sensor’s ability to intelligently enhance perception and create a more complete evaluation of a sensor system’s efficacy in improving the safety and performance of autonomous vehicles in real-world scenarios.