This article compares Frequency Modulated Continuous Wave (FMCW) and Time of Flight (ToF) for LiDAR. In summary, FMCW is better in longer term but ToF is still better now.
What are FMCW and ToF?
Lidar can take on several different modalities, which can be classified by their dependence on incoherence or coherence of the laser source which is used. Each of these methods uses the optical path of the target reflector to effectively modulate the envelope intensity of the detected signal. Time-of-flight (pulsed) and amplitude-modulated continuous-wave (AMCW) sensors detect range by measuring temporal properties of the received light intensity. Frequency-modulated continuous-wave (FMCW) and optical coherence tomographic (OCT) sensors map properties of the received optical field (amplitude and phase) into intensity and attempt to leverage the knowledge of both the amplitude and phase in order to detect range.
- Pulsed Time-of-Flight (TOF), often also known as dToF
TOF lidar uses the known fact that light travels at a fixed speed through a medium with a constant refractive index (3×108 m/s in air). Examples of pulsed time-of-flight (TOF) systems can be found in [12,13]. The transmitted pulse must be reflected by the target object, and collected by an aperture at the receiver. Range is measured by determining the difference in time of arrival and the time of transmission of the pulse. Fig. 1–1 shows a simple schematic outlining the operating principles of pulsed TOF lidar.
dToF often uses APD, SiPM or SPAD as receiver array.
2. Amplitude Modulated Continuous Wave (AMCW) Lidar
Amplitude-modulated continuous-wave (AMCW) lidar uses similar principles to TOF lidar, in that a target delay is measured at the receiver. However, in the case of AMCW, an intensity pattern is encoded on the transmitted optical power, such as a linear radio frequency chirp. For AMCW, the free-space path encodes a phase shift on the RF chirp, which can be detected accurately by measuring the intermediate frequency after mixing the received intensity signal with a non-delayed electronic version of the chirp. Examples of AMCW lidar systems have been studied in [15,16]. Fig. 1–2 shows a simple schematic outlining the operating principles of AMCW lidar.
3 Frequency Modulated Continuous Wave (FMCW) Lidar
Frequency-modulated continuous-wave (FMCW) lidar can analytically be shown as a comparable method to RF-chirped AMCW lidar, except where the chirped field is the optical field of a tunable laser. Where chirped AM lidar uses the laser as a carrier for an RF signal, and the RF signal is applied to the intensity of the light source, chirped FM lidar modulates the phase of the light source (usually a single-mode laser) such that the optical frequency of the light source is modulated directly. A free-space path encodes a phase shift on the optical chirp, and the phase shift is detected by mixing the reflected chirp with a non-delayed version of the chirp. This mixing occurs at the photodiode upon detection, so no special design beyond good detector design is needed to achieve this mixing effect. A schematic for FMCW lidar is shown in Fig. 1–3.
First, many LiDAR companies claims that FMCW is better than ToF. recent papers1–5 have presented a number of marketing claims about the benefits of Frequency Modulated Continuous Wave (FMCW) LiDAR systems.
This diagram illustrates the principal operation of a FMCW lidar. The low power transmit chirp (green) is reflected off an object. The frequency shift between the returning chirp (blue) is proportional to the distance and velocity of the object. An up and a down chirp are used to resolve for both values, distance and velocity.
The key reasons why FMCW is better in long run are:
- Better Ambient Light Immunity
Principle of ToF lidar operation during nighttime
Operation of ToF lidar in bright sunlight and in the presence of other lidars
2. Better Eye Safey
Commonly used lidar laser wavelengths plotted on top of terrestrial solar irradiance and visible spectrum
3. Better Signal to Noise
This diagram illustrates the concept of coherent amplification. The local oscillator, branched off from the blue transmit signal, interferes constructively with the weak purple receive signal and generates a new green strong beat frequency signal. The strong beat frequency signal is then fed back into and detected by the photodetector.
4. Get Both Distance and Velocity
SiLC’s silicon photonics integrated FMCW lidar chip provides depth and velocity data of every measured pixel. On the left is a camera image of a scene, followed by a depth and a velocity point cloud. Images courtesy of SiLC Technologies.
However, other LiDAR companies such as AEye believes that high shot-rate, agile-scanning TOF systems serve the needs of autonomous vehicle LiDAR more effectively than FMCW when cost, range, performance, and point cloud quality are important. However, it is not hard to see the logical reasoning where FMCW could play a niche role in applications where lower shot rates are suitable and FMCW systems are more economical. While there will be nice videos of FMCW and other low TRL systems in well controlled environments with expensive prototypes, it’s a whole different world when taking harsh environments and mass production into account. We hope this white paper will stimulate development and awareness in both TOF and FMCW systems, increasing the component options for perception engineers everywhere. The detailed info https://www.aeye.ai/technology/time-of-flight-vs-fmcw-lidar-a-side-by-side-comparison/
Some Interesting Arguments are below
- FMCW cannot measure lateral velocity simultaneously, in one shot, and has no benefit whatsoever in finding lateral velocity over ToF systems.
FMCW brings significant improvement in 4D sensing such as speed and better signal to noise. In the mean while FMCW is still on the leading edge or “bleeding” edge. Lots of R&D are required to be automotive grade, reliable, and readily scalable. The FMCW LiDAR such as Intel or Mobileye claims that FMCW will go into mass production around 2025, thanks to the innovation in Silicon Photonics.
- Aurora Team, “FMCW Lidar: The Self-Driving Game-Changer”, www.medium.com, April 9, 2020
- Philip Ross, “Aeva Unveils Lidar on a Chip”, IEEE Spectrum, December 11, 2019.
- Timothy Lee, “Two Apple veterans built a new lidar sensor — here’s how it works”, arsTECHNICA, October 2, 2018.
- Jeff Hect, “Lasers for Lidar: FMCW lidar: An alternative for self-driving cars”, LaserFocusWorld, May 31st, 2019.
- “Aeva launches ‘4D’ LiDAR on chip for autonomous driving”, www.optics.org, December 16, 2019.
- Phillip Sandborn, “FMCW Lidar: Scaling to the Chip-Level and Improving Phase-Noise-Limited Performance”, Electrical Engineering and Computer Sciences, University of California at Berkeley, Technical Report No. UCB/EECS-2019–148, December 1, 2019.
- “Technology readiness level”, Wikipedia
- A Gschwendtner, W Keicher, “Development of Coherent Laser Radar at Lincoln Laboratory”, MIT Tech journal, Vol 12, #2, 2000.
- C. Patel, “Stability of Single Frequency Lasers”, IEEE J Quantum Electronics, v4, 1968.
- Voxtel Laser Rangefinders, www.voxtel-inc.com, June 2020
- P Suni et al, “Photonic Integrated Circuit FMCW Lidar On A Chip”, 19th Coherent Laser Radar Conference