Recent papers have presented a number of marketing claims about the benefits of Frequency Modulated Continuous Wave (FMCW) LiDAR systems. As might be expected, there is more to the story than the headlines claim. This white paper examines these claims and offers a technical comparison of Time of Flight (ToF) vs. FMCW LiDAR for each of them. We hope this serves to outline some of the difficult system trade-offs a successful practitioner must overcome, thereby stimulating robust informed discussion, competition, and ultimately, improvement of both ToF and FMCW offerings to advance perception for autonomy.
Conventional metrics used for evaluating the performance of LiDAR for autonomous vehicles (such as frame rate, full frame resolution, and detection range) do not sufficiently address the challenges facing autonomous driving. In response, AEye proposes three new metrics for extending LiDAR evaluation, including: extending the metric of frame rate to include object revisit rate; expanding resolution to capture instantaneous resolution; and extending detection range to reflect the more critically important object classification range.
Based off of their findings, VSI Labs determined that AEye's iDAR system "can detect and potentially classify objects with enough precision, accuracy, and distance not possible with conventional LiDAR or camera sensors."
The artificial intelligence that drives autonomous vehicles will require artificial perception that is modeled after the greatest perception engine on the planet — the human visual cortex.