The race to build mass-market autonomous cars is creating big demand for laser sensors that help vehicles map their surroundings. But cheaper versions of the hardware currently used in experimental self-driving vehicles may not deliver the quality of data required for driving at highway speeds.
Most driverless cars make use of lidar sensors, which bounce laser beams off nearby objects to create 3-D maps of their surroundings. Lidar can provide better-quality data than radar and is superior to optical cameras because it is unaffected by variations in ambient light. You’ve probably seen the best-known example of a lidar sensor, produced by market leader Velodyne. It looks like a spinning coffee can perched atop cars developed by the likes of Waymo and Uber.
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 video 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…