When discussing autonomous vehicles, laser radar (LiDAR) technology plays a crucial role. LiDAR is a sensor capable of scanning the surrounding environment and generating three-dimensional images.

It is utilized for applications such as obstacle detection, mapping, and vehicle positioning in the context of autonomous driving.

Not too long ago, the high cost of LiDAR systems deterred many manufacturers. However, with technological advancements, prices have been gradually decreasing.

Nowadays, some mass-produced vehicles are equipped with one or even multiple LiDAR systems, providing robust support for the safety of assisted driving.

Principles and Types of LiDAR:

As a sensor, LiDAR employs laser beams to scan the environment, extracting information from the reflected signals. Typically comprising a transmitter and a receiver, the transmitter emits laser beams that scan the surroundings.

When the beams encounter objects, they are reflected, and the receiver captures the reflected laser, converting it into electrical signals. LiDAR systems can use either a rotating or fixed scanning system to survey the environment.

LiDAR facilitates the generation of three-dimensional point cloud images, a dataset composed of many three-dimensional coordinates representing the positions of reflected laser points in space.

Processing and analyzing this point of cloud data enable the identification of object locations, shapes, and sizes, crucial for the positioning and environmental perception of autonomous vehicles.

Applications of LiDAR in Autonomous Driving:

LiDAR finds widespread application in autonomous vehicles, contributing to obstacle detection, lane-keeping, adaptive cruise control, and autonomous parking. Among these, obstacle detection is a critical application.

Autonomous vehicles must rapidly and accurately identify obstacles in the surrounding environment, including other vehicles, pedestrians, buildings, and road signs. LiDAR's ability to generate high-precision three-dimensional point cloud images facilitates accurate environmental perception for autonomous driving.

As LiDAR technology continues to evolve, the emergence of new LiDAR types enhances the precision and reduces the cost of autonomous vehicles.

For instance, some new solid-state LiDARs utilize solid-state laser emitters and high-speed scanners, offering higher measurement accuracy and smaller form factors.

Additionally, optical LiDARs using visible light and infrared lasers can detect a broader range of obstacles, including black objects and low-reflectance surfaces.

These advancements broaden the prospects of LiDAR applications and offer more possibilities for the realization of autonomous driving.

Challenges and Future Developments:

Despite its advantages, LiDAR technology faces challenges, such as detection accuracy being affected by environmental interference and noise like rain, snow, and fog.

To enhance LiDAR detection accuracy, researchers are developing new algorithms and technologies, including machine learning and deep learning algorithms, as well as higher-resolution and higher-frame-rate sensor technologies.