Which is the best choice for autonomous driving: LiDAR or pure vision solution?

As a disruptive innovation in the field of modern transportation, autonomous driving technology has become a strategic focus for global automakers and technology companies. The core of autonomous driving technology lies in the ability of the vehicle to perceive the environment, which determines whether the system can make safe and effective decisions under complex road conditions. At present, perception technology is mainly divided into two categories: LiDAR and visual perception. LiDAR is widely used in the early development of autonomous driving technology because it can provide accurate distance and shape information. However, with the rapid progress of computer vision technology, pure visual perception solutions based on cameras have gradually emerged and have shown obvious advantages in certain scenarios.

LiDAR was originally considered to be an indispensable core hardware for achieving high-level autonomous driving . By emitting laser beams and receiving reflected signals, it can accurately measure the distance between objects and vehicles, and then build a three-dimensional environmental model to help the autonomous driving system achieve high-precision perception and navigation. However, with the maturity of visual perception technology, especially the application of deep learning and large-scale data training, the perception ability of pure visual solutions has been significantly improved. Tesla and other companies have achieved perception effects close to or even beyond LiDAR by integrating multiple cameras in vehicles and relying on powerful algorithm models.

In this context, this article will systematically analyze the technical applications and market development trends of LiDAR and pure vision solutions in autonomous driving. By discussing the advantages and disadvantages of both and typical application cases in detail, this article will explore in depth the factors that companies need to consider when choosing autonomous driving perception technology, in order to provide a useful reference for the development of the industry.

LiDAR Technology Analysis

1.1 Basic Principles of LiDAR

LiDAR (Light Detection and Ranging) is a technology that measures the distance between an object and a sensor by emitting laser light and receiving reflected light . Its basic working principle is to emit a short pulse of laser beam, which will be reflected back when it encounters the surface of an object. The sensor calculates the distance between the object and the LiDAR by detecting the time difference between the laser emission and reflection. By measuring the distance of multiple reflection points, LiDAR can generate a three-dimensional point cloud image that accurately depicts the geometry and object distribution of the surrounding environment.

The core components of LiDAR include laser emitters , optical systems, detectors , and control systems . The laser emitter generates and emits a laser beam of a specific wavelength, while the optical system focuses and guides the laser beam and directs the reflected light signal to the detector. The detector converts the received light signal into an electrical signal, and the control system calculates the distance information based on these electrical signals and generates a three-dimensional model of the environment. With the development of LiDAR technology, Frequency Modulated Continuous Wave (FMCW) LiDAR has become an emerging development direction for LiDAR. Compared with traditional pulsed LiDAR, FMCW LiDAR continuously emits frequency-modulated laser waves and obtains the distance and speed information of the target object by measuring the frequency difference. The advantage of FMCW LiDAR is that it can measure the speed and distance of multiple objects at the same time, with higher resolution and anti - interference ability. This technology is particularly effective in the detection of high-speed moving objects, and is particularly suitable for applications in highways and complex urban traffic environments. However, the technical implementation of FMCW LiDAR is complex and the manufacturing cost is high. The key technologies involved include high-precision frequency modulation, high-speed signal processing, and multi-target recognition, all of which place extremely high demands on the hardware and software of LiDAR . Therefore, although FMCW LiDAR has significant technical advantages, its commercialization process still faces challenges.

1.2 Advantages and Disadvantages of LiDAR Advantages: High-precision ranging

The biggest advantage of LiDAR is that it has a very high ranging accuracy, which can usually reach the centimeter level, much higher than traditional radar and camera technologies. Through high-density point cloud data, LiDAR can accurately perceive the position, shape and distance of objects in the surrounding environment, providing accurate environmental perception for autonomous driving systems.

All-weather working capability

LiDAR is not dependent on ambient lighting conditions and can work during the day, at night, and in environments with complex lighting. This makes LiDAR particularly suitable for changing outdoor environments, such as city streets, tunnels, and night driving. Unlike cameras, LiDAR is not affected by glare or backlight, so it can maintain stable perception capabilities under strong light conditions.

3D point cloud generation

LiDAR can generate high-precision 3D point cloud images, providing detailed spatial information about the environment. These point cloud data can be used for real-time obstacle detection, path planning, and environmental modeling, helping autonomous driving systems make correct decisions in complex environments.

Strong anti-interference ability

According to the wavelength of the laser, LiDAR is mainly divided into two types: 905nm and 1550nm, each with its own characteristics and application areas. The electromagnetic waves of LiDAR are not easily interfered by other electronic devices or environmental factors. Therefore, LiDAR can still maintain stable working performance in an environment with multiple electromagnetic signal interferences.

Disadvantages: High cost

The high manufacturing cost of LiDAR is a major obstacle to its large-scale application. High-precision laser emitters and detectors are expensive to produce, especially FMCW LiDAR, which has higher manufacturing costs due to its complex technology. In addition, the maintenance and calibration of the LiDAR system also require additional cost investment, which further increases the cost pressure of the entire vehicle. High system complexity The integration and debugging of the LiDAR system are highly complex and require deep integration with the vehicle's electronic and electrical architecture. LiDAR not only needs to be installed in a specific position on the vehicle to ensure that its perception field of view covers the surrounding environment, but also needs to fuse data with other perception systems (such as cameras and millimeter-wave radars ). This complex system integration requirement brings additional challenges to the development and testing of autonomous vehicles.

The weather has a big impact

Although LiDAR performs well at night and in complex lighting environments, in some severe weather conditions, such as fog, heavy rain or snow, the propagation of the laser beam will be seriously affected, resulting in a shortened detection distance and signal attenuation, which affects the perception accuracy. This makes the application of LiDAR in these weather conditions have certain limitations.

Heavy data processing burden

The amount of 3D point cloud data generated by LiDAR is huge, requiring powerful computing power for real-time processing. This places higher demands on the data processing and storage capabilities of the autonomous driving system, increasing the complexity and energy consumption of the system. In addition, the real-time transmission of high-density point cloud data also places higher bandwidth requirements on the in-vehicle network.

1.3 Application of LiDAR in Autonomous Driving

LiDAR technology is widely used in the field of autonomous driving, especially in L4 and above autonomous driving systems. The core perception modules of many autonomous driving systems rely on high-precision environmental data provided by LiDAR. For example, Waymo's autonomous driving vehicles are equipped with multiple types of LiDAR, including short-range and long-range LiDAR, to ensure accurate perception data in different driving scenarios.

On urban roads, LiDAR can help vehicles identify traffic lights, pedestrians, non-motorized vehicles, and complex building structures, ensuring that the autonomous driving system can drive safely in congested urban environments. On highways, LiDAR is mainly used to detect vehicles ahead, identify lane lines and road boundaries, and help the autonomous driving system perform safe high-speed driving and lane change operations.

In addition, LiDAR has also been widely used in autonomous parking systems. With the high-precision distance information provided by LiDAR, autonomous vehicles can accurately identify parking spaces and surrounding obstacles, and achieve efficient autonomous parking. Although the application of LiDAR in autonomous driving has made significant progress, its high cost and performance in bad weather have limited its large-scale commercialization process. As pure vision solutions gradually mature, LiDAR is gradually being abandoned by many companies in the autonomous driving market.

Technical analysis of pure visual solutions

2.1 Working Mechanism of Pure Vision Solution