How does a Self-driving Vehicle see using LiDAR?
By: Rahul Bhadani
Tags: Self Driving Cars, Lidar, Autonomous Vehicles, Autonomous Cars, Mobility

LiDAR point cloud. Image courtesy: data visualization by the author.

Autonomous vehicles have become a recent buzz word with several companies trying to create a perfect robotaxi such as Uber, Lyft, Cruise Automation, Motional, Aurora, Aptiv, Didi Chuxing, Yandex, etc. Well, recently Uber has sold its self-driving robotaxi division to Aurora citing operating costs. But that is not deterring other starts up to bring this technology to the realization. However, many people don’t that culmination of self-driving cars dates back to the DARPA Urban Challenge in 2007 where participating research teams were required to build a self-driving vehicle capable of driving in urban traffic, performing complicated maneuvers such as passing, merging, parking, and going through intersections. It was then several universities and private companies collaborated and participated

in the challenge bring the idea of autonomous vehicles to the mainstream.

Distance between objects and the LiDAR is determined by transmitted a laser pulse and measuring the time of the flight, i.e. the amount of the time it took to receive the backscattered pulse and knowing the speed of the light.

A key challenge in autonomous driving is determining how the car sees things around it. The early development of self-driving technology was using expensive sensors such as Velodyne 64E spinning LiDAR (Light Detection and Ranging) that costs a whopping $80,000, an array of camera sensors, Radar, and several control modules. However, in the last one and half decades, the technology has matured well and several start-ups have come into the market to provide low-cost perception sensors to provide “eyes” to cars to make the technology viable for commercial use. Some of the notable startups other than Velodyne who are making LiDAR technology more accessible are Luminar, Ouster, Xenomatix, Aeva, Innoviz, and Quanergy to name a few. A few of them such as Luminar and Velodyne have recently gone public via special purpose acquisition to tap on the public investment to accelerate the development of the LiDAR technology, especially focussing on solid-state LiDAR. A few others such as Innoviz, Ouster, and Aeva are planning to go public via a reverse merger by the first quarter of 2021. All said, you must be curious, how does a LiDAR sees and perceive the environment around it? I will provide brief details of LiDAR technology in this article.

LiDAR technology uses a laser, an extremely collimated beam of light, and a time-of-flight technique to create a 3D environment of its surrounding use point clouds representation. The LiDAR consists of a laser, a scanner, and optionally a GPS device. Usually, LiDAR uses 600nm — 1000nm wavelength of the light. Some of the widely known optical technology in scanning is (1) Phased-array, (2) Microelectromechanical mirrors (MEMS), (3) and Silicon chips without moving parts. Distance between objects and the LiDAR is determined by transmitted a laser pulse and measuring the time of the flight, i.e. the amount of the time it took to receive the backscattered pulse and knowing the speed of the light. Ultimately, LiDAR builds a complex map of the scene it encounters in the form of a point cloud. The point cloud can then be used by downstream analysis such as a computer vision or a machine-learning algorithm to get a detailed sense of the surroundings.

Flash LiDAR doesn’t require spinning mechanical parts and uses a wide-angle diverging to image the large area at once. However, its vertical and horizontal field of views (FOV) is limited and less than 360 degrees. On contrary, a spinning LiDAR uses a collimated beam but due to the imaging system being on a rotating turntable, it can have a horizontal field of view of 360 degrees. The downside of the spinning LiDARs is that they are bulky, heavy, and are prone to mechanical damage, and require careful handling. On the other hand, solid-state LiDARs are light-weight usually 100g — 1kg, and easier to handle. Researchers and vehicle engineers usually fuse sensor data from multiple LiDARs strategically mounted all around the vehicle to get 360 FOV, when using solid-state LiDARs.

Some of the challenges LiDARs must overcome are (1) direct sunlight (2) humidity (3) power consumption (4) operating temperature. Different LiDAR manufacturers overcome these difficulties in their own way through several innovative ideas such as selecting suitable operating wavelength, novel chip design, optical elements, and custom ASIC (Application-specific integrated circuit).

With the ongoing advancement in LiDAR technology, the future of mobility is not too far from us and one day we will be riding a robotaxi capable of navigating on its own.

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