Tag: Ford

IntroductionRecent papers1–5 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.
Competitive ClaimsBelow is a summary of our views and a side-by-side comparison between TOF vs. FMCW LiDAR claims.

Download “Time of Flight vs. FMCW LiDAR: A Side-by-Side Comparison” [pdf]
Claim #1: FMCW is a (new) revolutionary technologyThis is untrueContrary to the recent news articles, FMCW LiDAR has been around for a very long time, with its beginnings stemming from work done at MIT Lincoln Laboratory in the 1960s,8 only seven years after the laser itself was invented.9 Many of the lessons we learned about FMCW over the years—while unclassified and public domain—have unfortunately been long forgotten. What has changed in recent years is the higher availability of long coherence-length lasers. While this has justifiably rejuvenated interest in the established technology, as it can theoretically provide an extremely high signal gain, there are still several limitations, long ago identified, that must be addressed to make this LiDAR viable for autonomous vehicles. If not addressed, the claim that “new” FMCW will cost-effectively solve the automotive industry’s challenges with both scalable data collection and long-range, small object detections, will prove untrue.
Claim #2: FMCW detects/tracks objects farther, fasterThis is unprovenTOF LiDAR systems can offer very fast laser shot rates (several million shots per second in the AEye system), agile scanning, increased return salience, and the ability to apply high density Regions of Interest (ROIs)—giving you a factor of 2x–4x better information from returns versus other systems. By comparison, many low complexity FMCW systems are only capable of shot rates in the 10’s to 100’s of thousands of shots per second (~50x slower). So, in essence, we are comparing nanosecond dwell times and high repetition rates with tens of microsecond dwell times and low repetition rates (per laser/rx pair).
Detection, acquisition (classification), and tracking of objects at long range are all heavily influenced by laser shot rate, because higher laser shot density (in space and/or time) provides more information that allows for faster detection times and better noise filtering. AEye has demonstrated a system that is capable of multi-point detects of low reflectivity: small objects and pedestrians at over 200m, vehicles at 300m, and a class-3 truck at 1km range. This speaks to the ranging capability of TOF technology. Indeed, virtually all laser rangefinders use TOF, not FMCW, for distance ranging (e.g., the Voxtel rangefinder10 products, some with a 10+km detection range). Although recent articles claim that FMCW has superior range, we haven’t seen an FMCW system that can match the range of an advanced TOF system.
Claim #3: FMCW measures velocity and range more accurately and efficientlyThis is misleadingTOF systems, including AEye’s LiDAR, do require multiple laser shots to determine target velocity. This might seem like extra overhead when compared to the claims of FMCW with single shots. Much more important, is the understanding that not all velocity measurements are equal. While radial velocity in two cars moving head-on is urgent (one of the reasons a longer range of detection is so desired), so too is lateral velocity as it comprises over 90% of the most dangerous edge cases. Cars running a red light, swerving vehicles, pedestrians stepping into a street, all require lateral velocity for evasive decision making. FMCW cannot measure lateral velocity simultaneously, in one shot, and has no benefit whatsoever in finding lateral velocity over TOF systems.
Consider a car moving between 30 and 40 meters/second (~67 to 89 MPH) detected by a laser shot. If a second laser shot is taken a short period later, say 50us after the first, the target will only have moved ~1.75mm during that interval. To establish a velocity that is statistically significant, the target should have moved at least 2cm, which takes about 500us (while requiring sufficient SNR to interpolate range samples). With that second measurement, a statistically significant range and velocity can be established within a time frame that is negligible compared to a frame rate. With an agile scanner, such as the one AEye has developed, the 500us is not solely dedicated or “captive” to velocity estimation. Instead, many other shots can be fired at targets in the interim. We can use the time wisely to look at other areas/targets before returning to the original target for a high confidence velocity measurement. Whereas, an FMCW system is captive for their entire dwell time.
Compounding the captivity time is the additional fact that FMCW often requires a minimum of two laser frequency sweeps (up and down) to form an unambiguous detection, with the down sweep providing information needed to overcome ambiguity arising from the mixing range + Doppler shift. This doubles the dwell time required per shot above and beyond that already described in the previous paragraph. The amount of motion of a target in 10us can be typically only 0.5mm. This level of displacement enters the regime where it is difficult to separate vibration versus real, lineal motion. Again, in the case of lateral velocity, no FMCW system will instantly detect lateral speed at all without multi-position estimates such as those used by TOF systems, but with the additional baggage of long FMCW dwell times.
Lastly, in an extreme TOF example, the AEye system has demonstrated detected objects at 1km. Even if it required two consecutive shots to get velocity on a target at 1km, it’s easy to see how that would be superior to a single shot at 100m given a common frame rate of 20Hz and typical vehicle speeds.
Claim #4: FMCW has less interferenceQuite the opposite actually!Spurious reflections arise in both TOF and FMCW systems. These can include retroreflector anomalies like “halos,” “shells,” first surface reflections (even worse behind windshields), off-axis spatial sidelobes, as well as multipath, and clutter. The key to any good LiDAR is to suppress sidelobes in both the spatial domain (with good optics) and the temporal/waveform domain. TOF and FMCW are comparable in spatial behavior, but where FMCW truly suffers is in the time domain/waveform domain when high contrast targets are present.
ClutterFMCW relies on window-based sidelobe rejection to address self-interference (clutter) which is far less robust than TOF, which has no sidelobes to begin with. To provide context, a 10us FMCW pulse spreads light radially across 1.5km range. Any objects within this range extent will be caught in the FFT (time) sidelobes. Even a shorter 1us FMCW pulse can be corrupted by high intensity clutter 150m away. The 1st sidelobe of a Rectangular Window FFT is well known to be -13dB, far above the levels needed for a consistently good point cloud. (Unless no object in the shot differs in intensity by any other range point in a shot by more than about 13dB, something that is unlikely in operational road conditions).
Of course, deeper sidelobe taper can be applied, but at the sacrifice of pulse broadening. Furthermore, nonlinearities in the receiver front end (so-called spurious-free dynamic range) will limit the effective overall system sidelobe levels achievable due to: compression and ADC spurs (third order intercepts); phase noise;6 and atmospheric phase modulation etc., which no amount of window taper can mitigate. Aerospace and defense systems of course can and do overcome such limitations, but we are unaware of any low-cost automotive grade systems capable of the time-instantaneous >100db dynamic range required to sort out long-range small objects from near-range retroreflectors, such as arise in FMCW.
In contrast, a typical Gaussian TOF system, at 2ns pulse duration, has no time-based sidelobes whatsoever beyond the few cm of the pulse duration itself. No amount of dynamic range between small and large offset returns has any effect on the light incident on the photodetector when the small target return is captured. We invite anyone evaluating LiDAR systems to carefully inspect the point cloud quality of TOF vs FMCW under various driving conditions for themselves. The multitude of potential sidelobes in FMCW lead to artifacts that impact not just local range samples, but the entire returned waveform for a given pulse!
First surface (e.g., FMCW behind a windshield or other first surface)A potentially stronger interference source is a reflection caused by either a windshield or other first surface that is applied to the LiDAR system. Just as the transmit beam is on near continuously, the reflections will be continuous, and very strong, relative to distant objects, representing a similar kind of low frequency component that creates undesirable FFT sidelobes in the transformed data. The result can also be a significant reduction of usable dynamic range. Furthermore, windshields, being multilayer glass under mechanical stress, have complex inhomogeneous polarization. This randomizes the electric field of the signal return on the photodetector surface complicating (decohering) optical mixing.
Lastly, due to the nature of the time domain processing vs frequency domain processing, the handling of multi-echoes—even with high dynamic range—is a straightforward process in TOF systems. Whereas, it requires significant disambiguation in FMCW systems. Multi-echo processing is..