High cost of hi-res LIDAR slows progress of self-driving cars
The self-driving car industry may be held back by the high price of high-resolution LIDAR sensors. Many companies, such as Velodyne and Quanergy, are working on solid-state LIDAR devices, which are less costly to produce at scale, but experts have warned they lack the finely detailed images self-driving cars rely on to navigate. "If you only see a target out at 30 metres or so, at freeway speeds that’s a fraction of a second," said Austin Russell, the CEO of LIDAR start-up Luminar.
But not all lidar sensors are created equal. Velodyne, for example, has a range of offerings. Its high-end model is an $80,000 behemoth called HDL-64E—this is the one that looks a lot like a coffee can. It spits 64 laser beams, one atop the other. Each beam is separated by an angle of 0.4° (smaller angles between beams equal higher resolution), with a range of 120 meters. At the other end the firm sells the smaller Puck for $8,000. This sensor uses 16 beams of light, each separated by 2.0°, and has a range of 100 meters.

To see what those numbers mean, look at the videos below. It shows raw data from the HDL-64E at the top, and the Puck at the bottom. The expensive sensor’s 64 horizontal lines render the scene in detail, while the image produced by its cheaper sibling makes it harder to spot objects until they’re much closer to the car. While both sensors nominally have a similar range, the lower resolution of the Puck makes it less useful for obstacles until they are much closer to the vehicle.

At 70 miles per hour, spotting an object at, say, 60 meters out provides two seconds to react. But when traveling at that speed, it can take 100 meters to slow to a stop. A useful range of somewhere closer to 200 meters is a better target to shoot for to make autonomous cars truly safe.

That’s where cost comes in. Even an $8,000 sensor would be a huge problem for any automaker looking to build a self-driving car that a normal person could afford. Because of this, many sensor makers are readying new kinds of solid-state lidar devices, which use an array of tiny antennas to steer a laser beam electronically instead of mechanically. These devices promise to be easier to manufacture at scale and cheaper than their mechanical brethren. That would make them a palatable option for car companies, many of which are looking to build autonomous cars for the mass market as soon as 2021.

But some of these new solid-state devices may currently lack the fidelity required for self-driving cars to operate safely and reliably at highway speeds.

The French auto parts maker Valeo, for example, claims to have built what it says is the world’s first laser scanner for cars that’s ready for high-volume production, the SCALA. It features four lines of data with an angular resolution of 0.8°. Automotive News previously reported that Valeo will provide the lidar sensor used in the new Audi A8, though at the time of writing Audi declined to confirm this and Valeo didn’t respond to a request for details. The new A8 is the first production car to feature lidar and can drive itself—but only in heavy traffic at speeds less than 37 miles per hour.

In June, Graeme Smith, chief executive of the Oxford University autonomous driving spinoff Oxbotica, told MIT Technology Review that he thinks a trade-off between data quality and affordability in the lidar sector might affect the rate at which high-speed autonomous vehicles take to the roads. “Low-speed applications may be more affordable more quickly than higher-speed ones,” he explained. “If you want a laser that’s operating over 250 meters, you need a finely calibrated laser. If you’re working in a lower-speed environment and can get by with 15 meters’ range, then you can afford [to use] a much lower-cost sensor.”

Austin Russell, the CEO of lidar startup Luminar, says his company actively chose not to use solid-state hardware in its sensors, because it believes that while mechanically steering a beam is more expensive, it currently provides more finely detailed images that are critical for safe driving. “It doesn't matter how much machine-learning magic you throw at a couple of points [on an object], you can’t know what it is,” he says. “If you only see a target out at 30 meters or so, at freeway speeds that’s a fraction of a second.”