When we talk about self-driving cars, LIDAR technology is a very important part of the equation. LIDAR is a sensor that scans its surroundings and generates a three-dimensional image. It can be used in application scenarios such as identifying obstacles, building maps and locating vehicles. And some time ago, with the successful landing of Wo Sai Technology NASDAQ, China's "LIDAR first stock", but also for the LIDAR industry injected a strong dose of cardiotonic agent.
Once upon a time, the expensive price of LIDAR makes many manufacturers deterred, and with the update of technology, the price is step by step down, now part of the production car has been able to equip one or even more LIDAR, for the assisted driving safety to provide a strong guarantee. This article will lead readers to a detailed explanation of the hottest and most important sensor in the field of autonomous driving - LIDAR.
The principle and types of LIDAR LIDAR acts as a sensor that uses a laser beam to scan its surroundings and obtain information from the reflected signals returned. LIDAR usually consists of a transmitter and a receiver. The transmitter emits a laser beam which scans the surroundings. When the laser beam encounters an object, it is reflected back and the receiver picks up the reflected laser light and converts it into an electrical signal. LIDAR can use either a rotating scanning system or a fixed scanning system to scan the surroundings.
A three-dimensional point cloud image can be generated using LIDAR. A point cloud is a collection of data consisting of many three-dimensional coordinates that represent the position in space of points reflected back from a laser beam. By processing and analyzing the point cloud data, the location, shape and size of an object can be identified.
This information is very important for the localization and environment sensing of self-driving cars.
There are many different types of LIDAR available in the market today.
These LIDARs have different characteristics and applicable scenarios. According to the different scanning methods, LIDAR can be categorized into rotating LIDAR and fixed LIDAR.
Rotating LIDAR scans the surrounding environment by rotating the scanning head. It usually contains a rotating scanner and a control system. The scanner emits a laser beam into the environment and rotates after emission to scan the environment once. The control system converts the scan data into a 3D point cloud image. The advantage of the rotating LIDAR is that it scans quickly and captures details of the surroundings. However, it has the disadvantage of being costly as it requires sophisticated scanners and control systems.
Stationary LIDAR involves directing multiple laser beams in different directions and then measuring the returned reflected signals simultaneously. Since the laser beams are emitted simultaneously, stationary LIDAR is more compact and cost effective as it does not require a rotating scanning head. It has the advantage of being less expensive and more accurate, but it does not capture the details of the surroundings.
Apart from rotating and fixed LIDAR, there are other types of LIDAR. For example, single-line LIDAR uses only one laser beam to scan the environment and is suitable for low-speed autonomous driving scenarios. Multi-line LIDAR, on the other hand, uses multiple laser beams, which can increase scanning speed and resolution. There are also emerging LIDAR technologies, such as solid-state LIDAR and optical LIDAR, that offer higher accuracy and lower cost.
LIDAR in autonomous driving
LIDAR has a wide range of applications in self-driving cars. It can be used in application scenarios such as obstacle detection, lane keeping, adaptive cruise control and autonomous parking. Among them, obstacle detection is one of the most critical applications. Autonomous vehicles need to be able to quickly and accurately recognize obstacles in the surrounding environment, including other vehicles, pedestrians, buildings and road signs. LIDAR can generate high-precision 3D point cloud images to recognize the location and size of obstacles, providing accurate environment sensing for self-driving cars.
LIDAR technology continues to evolve, and new types of LIDAR are emerging to provide higher accuracy and lower cost for self-driving cars. For example, some new solid-state LIDARs use solid-state laser emitters and high-speed scanners, giving them higher measurement accuracy and smaller size. In addition, some new optical LIDARs use visible and infrared lasers to detect more types of obstacles, including black objects and low-reflectivity surfaces. The emergence of these new technologies makes LIDAR more promising and opens up more possibilities for the realization of self-driving cars.
In addition, LIDAR technology faces several other challenges. For example, the detection accuracy of LIDAR is affected by the interference and noise of the environment, such as rain, snow and fog. In order to improve the detection accuracy of LIDAR, researchers are developing new algorithms and techniques, such as algorithms based on machine learning and deep learning, as well as sensor technologies with higher resolution and higher frame rate.
Competition between domestic and foreign LIDAR vendors
Chinese LIDAR vendors have performed well in terms of independent R&D, technology maturity, and market share. roboSense, Hesai Technology, and SureStar have all launched a variety of LIDAR products and have been widely used in autonomous driving, robotics, logistics, and other fields. Especially in terms of cost, the products of Chinese LIDAR vendors are relatively more affordable, which also provides an advantage for them in the market competition. With the listing of Hesai, it has pushed the domestic LIDAR to an unprecedented new height.
However, at the same time, in the R & D and application of LIDAR technology, the United States and Israel and other developed countries have a good performance. Velodyne and Quanergy and other U.S. LIDAR manufacturers in the mechanical LIDAR and solid-state LIDAR has a high degree of technological maturity, product stability and accuracy have reached a high level. Israeli LIDAR manufacturer, in the solid-state LIDAR and MEMS LIDAR technology results also attracted much attention. In particular, the solid-state LIDAR products launched by Innoviz are considered to be one of the most disruptive products on the market, featuring high precision, high speed and high stability.
In addition, in addition to technical strength, LIDAR manufacturers also need to focus on international market competition. Due to China's rapid development in LIDAR technology and product R&D, domestic LIDAR vendors are accelerating their move to the international market.Chinese LIDAR vendors such as RoboSense and Hesai Technology have already set up branches in the US, Europe, and Japan, and established cooperative relationships with local automakers and autonomous driving companies, accelerating their own internationalization process.
However, the competition in the international market is also very fierce. In the U.S. market, Velodyne has been occupying an absolute market share and possesses strong technological and brand advantages. In addition, companies such as Quanergy and Innoviz have also gained a certain market share in the U.S. market. In the European market, LeddarTech and Cepton have also performed well, competing fiercely with Chinese manufacturers.
Summarize
As one of the core sensors of self-driving cars, LIDAR can provide high-precision environment sensing and obstacle detection, which is one of the key technologies to realize autonomous driving. The technology of LIDAR continues to evolve, and the emergence of new types of LIDAR, such as solid-state LIDAR and optical LIDAR, provides higher accuracy and lower cost for self-driving cars. With the continuous development of self-driving cars, the application prospect of LIDAR will be more and more broad.
However, LIDAR also has some limitations. First, it is sensitive to the reflectivity and color of the environment, and black objects in the environment may not be detected. Second, LIDAR's measurement distance is limited, and detection over long distances can lead to a decrease in accuracy. To overcome these limitations, researchers are actively exploring new sensor technologies and algorithms to improve the sensing ability and safety of self-driving cars.
In conclusion, LIDAR, as one of the core sensors for self-driving cars, has a wide range of applications and evolving technologies. We believe that in the near future, LIDAR technology will continue to move forward as an important technology in the field of autonomous driving, providing more accurate, safe and reliable environment sensing capability for self-driving cars. Translated with www.DeepL.com/Translator (free version)
