FMCW LiDAR News: Aeva, SILC, Scantinel

Aeva Q1 2021 Investor Presentation shows the company's technology advantages:

BusinessWire:  SiLC Technologies announced a $17M Series A funding round. Following a $12M seed round in 2020, the new financing brings SiLC’s total funding to over $30M.

SILC develops what it calls the industry's first and only fully integrated FMCW 4D imaging chip:

BusinessWire: Scantinel which develops FMCW LiDAR for autonomous vehicles has received Series A financing from Scania Growth Capital.

Forbes contributor Sabbir Rangwala publishes an article "The LiDAR Range Wars - Mine Is Longer Than Yours" discussing the trade-offs between the range, latency, resolution, and false alarm and detection probabilities:

"Princeton Lightwave and Luminar were the first to announce and demonstrate 200-300 m ranges with their 15XX nm automotive LiDAR. Aeva announced recently that their 15XX nm Frequency Modulated Continuous Wave (FMCW) LiDAR can detect cars at 500 m and pedestrians at 350 m. Prior to this, Aeye advertised a 1000 m range for detection of cars. Not to be outdone, Argo announced recently that its Geiger Mode LiDAR operating at > 1400 nm wavelength has a range of 400 m (Argo acquired Princeton Lightwave in 2017).

LiDAR range is important for L4 autonomous vehicles (AVs), but the specification is nuanced. For safety critical obstacle avoidance, the AV perception engine needs to recognize road hazards in adequate time to enable safety maneuvers like braking to avoid tire debris. What matters is the range for a particular object reflectivity (10% seems to be a reasonable standard) and a high confidence level (> 99%) of the hazard recognition (otherwise, the false alarm rate would be very high, causing constant braking and leading to passenger discomfort and complaints). It is worth noting that there is a difference between detection (”something is out there but we don’t know what it is”) and recognition (”the something out there is a stalled car or a pedestrian”). Too often, range numbers are thrown around that relate to detection, which is generally not actionable. Recognition is a more difficult problem that relies on the resolution (see below) and the accuracy of real time image processing."