What is LiDAR in autonomous driving?

LiDAR (Light Detection and Ranging) is a core sensor in autonomous driving systems. It accurately detects the distance, shape, and speed of target objects by emitting laser beams and measuring reflected signals. The working principle is based on Time of Flight (ToF) or Frequency-Modulated Continuous Wave (FMCW) technology. By calculating the time difference between the emission and reception of laser pulses and combining it with angle information, LiDAR generates high-precision 3D point cloud data to construct a digital model of the vehicle's surrounding environment in real time. Compared to cameras and millimeter-wave radars, LiDAR offers centimeter-level ranging accuracy, strong resistance to light interference (e.g., backlight or nighttime conditions), and effective identification of low-reflectivity objects (such as black vehicles). However, its performance is significantly affected by heavy fog or rain. Currently, mainstream automotive LiDAR systems operate at 905nm or 1550nm wavelengths, with 1550nm providing superior long-range detection and weather penetration capabilities, albeit at higher cost. Technologically, mechanical rotating designs are gradually being replaced by solid-state solutions (e.g., MEMS, optical phased arrays) to enhance reliability and reduce costs. In autonomous driving applications, LiDAR performs critical functions including environmental modeling, obstacle classification, and lane-level positioning, typically integrated with cameras and millimeter-wave radars in multi-sensor fusion systems to ensure perception redundancy. Modern 128-line high-resolution LiDAR systems can now produce detailed point cloud imagery, while chip-scale technologies are driving further miniaturization and mass-market adoption.