Who’s the Lidar IP Leader?

发布时间:2018-04-24 00:00
作者:Ameya360
来源:Junko Yoshida
阅读量:1072

Among the host of sensors nowadays loaded into autonomous vehicles, lidar (light detection and ranging) projects as both critical and lucrative. 

As the automotive industry girds for a wave of autonomous car rollouts, Pierre Cambou, activity leader for imaging and sensors at market-research firm Yole Développement (Lyon, France), said he can’t imagine a robotic vehicle without lidars. “You need a lidar,” he noted.

Yole forecasts that revenue generated by lidars will reach $1.6 billion in 2022 and will balloon to $31.5 billion by 2032.

However, the technologies that drive lidars are still in flux, with new developments still in he pipeline. As Akhilesh Kona, senior analyst for automotive electronics and semiconductors at IHS Markit, previously told EE Times, lidar technology suppliers continue to improve durability, size, and cost by developing a variety of beam-steering technologies that range from mechanical to MEMS and solid-state.

As the race for better lidar heats up, the inevitable question is: Who’s the lidar leader? One way to find out is to look at lidar-related patents filed. 

Knowmade, one of Yole's group companies that specializes in IP analysis and patent assessment, recently examined lidar devices and systems for automotive. Knowmade identified more than 6,480 lidar-related patent families for automotive.

Although this patent activity began as early as the late 1960s, the number of patent publications has exploded in the last several years. In particular, between 2007 and 2017, lidar patents had an annual compound growth rate of 21 percent.

In the early days, companies such as Bosch, Denso and Valeo dominated patent filings related to automotive lidars. Paul Leclaire, technology and patent analyst at Knowmade, describes these as “historical IP players.” Their patents are mostly related to “ADAS applications, based on incremental technologies, and with limited amount of white spaces,” he observed. By “an area with a limited amount of ‘white spaces’ means that it is difficult to file a patent with claims that do not overlap other patent claims,” he explained. “Thus, new patent applications have less chance to be granted.”

However, those historical IP players’ activities alone cannot explain the recent escalation in lidar IPs, Leclaire said.

The newcomers in lidar break down in several categories.

Semiconductor companies’ lidar IPs

The first group consists of semiconductor companies such as Qualcomm, LG Innotek, Ricoh and Texas Instruments. Their contributions are “reducing the size of lidars” and “increasing the speed with high pulse rate” by using non-scanning technologies, Leclaire explained. These players’ patents offer hints as to how beam-steering has become the new preferred mode, and how the market is beginning to see the emergence of compounds (detector, laser) specifically dedicated to lidars, he added.

Pure IP players

Another set of newcomers includes Quanergy, Velodyne, Luminar and LeddarTech. Leclaire calls them “pure IP players” dedicated to lidar development. Their patent publications focus on highly specific patented technology that leads to product assertion and its application.

Notable is the emergence of lidar IP players in China. They include LeiShen, Robosense, Hesai, Bowei Sensor Tech. “The main Chinese industrial players are IP newcomers that have entered the IP landscape only two or three years ago. The vast majority of their patent applications are still pending and have not been extended to countries other than China,” Leclaire told us. “Their IP portfolios are, however, related to their lidar products that are currently on the market.”

Asked if there are other Chinese lidar IP players, he said that the four mentioned are the most notable. Other Chinese players are mostly academic players. 

Robotic vehicle vendors

The last group of newbies to lidar are autonomous carmakers themselves. They are using lidar as “tools to provide complex embedded sensor systems,” said Leclaire. Active in the IP landscape are Google, Waymo, Uber, Zoox and Faraday Future. Chinese giants such as Baidu and Chery also have lidar IPs.

For most robotic vehicle vendors, lidar is a central component of their patented sensing solutions. Many of their patents are related to method and process of “computing,” according to Knowmade. 

Asked about “promising technologies” likely to accelerate development of lidars, Leclaire pointed out two: new light sources such as laser and VCSEL (vertical-cavity surface-emitting laser), and beamforming technologies. 

The use of VCSEL as a novel type of laser source has advantages that include “small angular divergence, VCSEL array, high output power,” he noted. 
Similarly, methods to perform beamforming and beam steering operations are more and more described in patents related to solid-state lidar, Leclaire observed.

Asked if Knowmade can identify who dominates in IPs related to new light sources or beamforming, Leclaire said he can’t. His team’s analysis only focuses on lidar devices and systems for automotive. “We have analyzed neither the IP landscape of lidar components like laser, VCSEL, photodetectors, SPAD (single photon avalanche detector), APD (avalanche photodiodes), nor the IP landscape related to beamforming technologies.”

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LiDAR Goes Back To The Future
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Autonomous and assisted driving  Still, the biggest opportunity for LiDAR is automotive, and work is underway to reduce the number of moving parts and the cost of these systems with solid-state designs.  “To me, LiDAR is a really interesting system because there are so interesting components in there that all have relevance to semiconductors,” says Jeff Miller, product strategist for Mentor, a Siemens Business. “There’s some very interesting work being done in the silicon photonics area to try to make solid-state LiDARs. And there’s some very interesting work in the more traditional LiDAR space in terms of how do I get power to the laser, how do I control my laser pulses, and very interesting power transistor designs. And then there’s the data processing angle on this. What do I do with this enormous volume of data I’m producing in these 3D point clouds that come off the LiDAR sensor? How do I make any kind of sense of that? 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