I. Ranging based on millimeter-wave radar
Millimeter-wave radar is a mature technology for distance estimation, initially applied in adaptive cruise control. After Infineon introduced its 24GHz monolithic radar solution, millimeter-wave radar was incorporated into various ADAS modules, with global shipments reaching tens of millions. Millimeter-wave radar is used as a sensor in the distance estimation of autonomous vehicles for tasks such as obstacle recognition and ranging.
Currently, the global market share of millimeter-wave radar is mainly monopolized by leading international suppliers such as Bosch and Continental. However, with the development of the domestic market, domestic suppliers like Huayu Automotive are also investing heavily in millimeter-wave radar. Data shows that the market is projected to reach approximately 32 billion yuan by 2025.
Millimeter-wave radar, which operates in the millimeter-wave band, is essentially an electromagnetic wave with a wavelength of approximately 1-10 mm. It determines the position and distance of obstacles by transmitting millimeter waves and receiving the echoes, based on the time difference between transmission and reception. The primary distance estimation method for millimeter-wave radar is the FMCW modulation method.
The principle is to generate a continuously changing signal through an oscillator. There will be a frequency difference between the transmitted and received signals. This frequency difference is linearly related to the difference between the transmission and reception time of the millimeter wave. By measuring the frequency difference, the distance between the vehicle and the object in front can be estimated.
Millimeter-wave radar is widely used in ADAS (Advanced Driver Assistance Systems), primarily for Automatic Emergency Braking (AEB), Forward Collision Warning (FCW), Active Lane Control (ALC), Blind Spot Detection (BSD), and Lane Change Assist (LCA). It boasts advantages such as good anti-interference capabilities, easy penetration of rain and snow, good adaptability, all-weather operation, relatively mature technology, and low cost. However, it also has drawbacks such as low resolution. Millimeter-wave radar has become an indispensable primary sensor for distance estimation in autonomous vehicles.
II. LiDAR-based ranging
With the continuous evolution of autonomous vehicles, LiDAR-based distance estimation has become an indispensable sensor in L3 and higher level autonomous vehicles due to its unique 3D environment modeling capabilities. From mechanical to hybrid solid-state, and then to pure solid-state LiDAR, the cost of LiDAR is constantly decreasing with technological advancements, and it is developing towards miniaturization and ASIC integration, making it the most crucial component of autonomous driving sensors.
Currently, most autonomous vehicle test vehicles use mechanical LiDAR. However, mechanical LiDAR is expensive, has complex manufacturing processes, and a short lifespan, making it difficult to meet the stringent requirements of future autonomous vehicles. Hybrid solid-state LiDAR represents an intermediate product in the transition from mechanical to pure solid-state LiDAR. Solid-state LiDAR mainly consists of three types: MEMS, OPA, and 3D Flash. Its debugging can be automated, and it has no mechanical rotating parts, resulting in significant improvements in cost and practicality. Solid-state LiDAR is undoubtedly the future trend of LiDAR development.
LiDAR-based distance estimation for autonomous vehicles works by using laser as a carrier wave; it is a type of radar operating in the optical frequency band. Its operating principle involves emitting a laser beam towards the object being measured, then comparing the received echo with the emitted signal, and after appropriate processing, obtaining relevant information about the object, such as its distance and orientation.
LiDAR is mainly used for acquiring depth information, obstacle detection, and target recognition. Its main advantages include the ability to create 3D models of surrounding objects to form high-definition images, facilitating computer processing and recognition. It also boasts advantages such as good directionality, no electromagnetic interference, comprehensive information acquisition, and accurate detection. However, it is susceptible to environmental influences, its accuracy decreases in adverse weather conditions, making obstacle recognition difficult, and it is relatively expensive. Camera-based distance estimation plays a crucial role in distance estimation for autonomous vehicles, often referred to as the "eyes" of autonomous driving. Camera technology is the most mature and was the earliest to be applied in vehicles, serving as the primary visual sensor in the ADAS (Advanced Driver Assistance Systems) stage. This includes monocular and binocular cameras.
