Automotive LiDAR sees New Product Launches, OEM Partnerships and Big Investments

Subscribe To Download This Insight

By James Hodgson | 4Q 2017 | IN-4772

The third quarter of 2017 saw numerous announcements relating to investments, new product launches and new partnerships in the automotive LiDAR market.

Registered users can unlock up to five pieces of premium content each month.

Log in or register to unlock this Insight.

 

Product Launches, New Partnerships, Big Investments

NEWS


The third quarter of 2017 saw numerous announcements relating to investments, new product launches and new partnerships in the automotive LiDAR market. A summary of the relevant news is given below:

  • Innoviz Raises US$65 million investment: The ambitious MEMs-based solid-state LiDAR startup first announced their Innoviz One solution in 2016, more recently announcing their InnovizPro solution targeted at prototype development and more short/medium term implementations. The startup raised an impressive US$65 million in funding with investors including Tier 1 Suppliers Magna and Delphi Automotive, with the former having already entered into a public partnership with Innoviz.
  • LeddarTech Raises US$101 million investment: Canadian solid-state sensor developer LeddarTech announced the close of a US$101 million funding round, with investors that include optical technology developer Osram, and Tier 1 suppliers Magneti Marelli and Delphi Automotive. The firm has developed solid-state LiDAR ASICs for many years, and had previously announced a deal to supply Tier 1 supplier Valeo. To accompany their undisclosed investment, Delphi announced a commercial partnership with LeddarTech to further develop automotive-grade LiDAR technology.
  • Pioneer Launches Solid-state LiDAR Sensor: Automotive supplier announced the development of a MEMs-based solid-state LIDAR sensor, with production slated for 2020.
  • Velodyne named as Supplier to Mercedes-Benz: At the IAA conference Frankfurt, Velodyne announced that they had been selected by Mercedes-Benz to supply the OEM’s Research and Development team with Ultra-puck VLP-32C LiDAR sensors. 

Solid-State Leading the Charge

IMPACT


Since its invention in 2005, LiDAR has been a permanent fixture for autonomous prototypes and trials, but has often been derided as too expensive and too fragile for implementation on consumer vehicles at scale. Numerous vendors and startups have sought to ruggedize the technology and drag down ASPs through the development of a solid-state sensor, with optical phased arrays, flash LiDARs and MEMs-based beam steering all being put forward as viable candidates. The announcements above show that the momentum is doubtlessly with MEMs-based solutions which, though not technically solid-state on a micro scale, have the most promise of delivering high-performance and affordability in the medium term, thanks to the re-use of a technology well established in parallel industries and advancements in DSP techniques.

A Strategic Change of Direction for Delphi, or just Hedging Bets?

COMMENTARY


Delphi has had a long-term interest in automotive LiDAR, having previously worked closely with and invested heavily in Quanergy and their unique optical phased array solution. It was therefore surprising to some that Delphi should invest in not one, but two of Quanergy’s direct competitors within the space of a week. Is Delphi simply hedging its bets? Partially – yes. However, it is important to recognize the distinction between LeddarTech’s and Innoviz’s solutions. While the latter is more focused on long range obstacle detection ahead of the vehicle, LeddarTech’s solutions are better positioned to provide the autonomous vehicle with a short-range 360° perception.

More worrying for Quanergy are the parallels that can be drawn between their own S3 solution and the Innoviz One. Both operate at comparable regions, and have similar target ASPs. Furthermore, the Innoviz solution supports mutli-frame mode and adaptive interrogation of specific regions of interest – features which used to be the preserve of Quanergy’s OPS solution. The Onus is clearly on Quanergy to demonstrate that their approach is as practicable as it is academically interesting.