London, United Kingdom - 25 May 2017

As multiple stakeholders push to introduce highly autonomous driving in advance of ambitious deadlines, the need to develop a suite of sensors that guarantee robust perception is top priority. Many established vendors and innovative startups are positioning solid-state light detection and ranging (LiDAR) technologies as a cost-effective means to industrialize reliable obstacle detection and simultaneous localization and mapping (SLAM) in autonomous vehicles. ABI Research expects solutions to come to market beginning in 2019, with the market value nearing US$13 billion by 2027.

“Even the most aggressive startups do not expect LiDAR to become a silver bullet solution for autonomous vehicle sensing and perception, but the technology’s natural characteristics certainly fit nicely with those of radar and camera, which have been the staples of obstacle detection in the ADAS market,” says James Hodgson, Senior Analyst at ABI Research. “The biggest barrier to adoption is cost, with solid-state beam steering and IR illumination technologies proving essential for feasible commercial implementation.”

A broad range of vendors are developing solid-state solutions, including startups such as Aerostar Strobe, Innoviz Technologies, Phantom Intelligence and Quanergy, as well as established players like Ibeo, LeddarTech, Pioneer, and Sensata. Furthermore, Velodyne, a longtime market leader with its mechanical scanning solutions, recently announced its own solid-state Velarray solution, which is intended to be priced in the hundreds of dollars when shipping at scale.

“The target price per sensor of $100 to $250, if reached, would represent a huge reduction from historic prices,” concludes Hodgson. “Nevertheless, the need to fit multiple sensors in a connected car to provide 360-degree coverage means that equipping a vehicle with LiDAR will still represent a significant cost delta, even when the units are shipping at scale. Therefore, simply enabling legacy collision avoidance ADAS will not represent a sufficient ROI, and LiDAR must support the highly-automated driving use case to warrant the investment.”