Which One Is More Beneficial to the Safety of Intelligent Driving: Millimeter-wave Radar, LiDAR, or Camera?

Intelligent driving refers to the use of advanced technologies and systems to enhance the safety, efficiency, and comfort of driving. It involves the integration of LiDAR, cameras, radar, and other devices that can perceive the environment around a vehicle and provide real-time data to assist with decision-making.

By combining these technologies, intelligent driving systems can create a detailed picture of the surrounding environment in real-time, allowing vehicles to make informed decisions about speed, direction, and braking based on current conditions on the road ahead. 

Intelligent driving technology is the future development trend. In the application of intelligent driving, multi-sensor fusion is inevitable. Millimeter-wave radar, LiDAR, and cameras have their own advantages and disadvantages. Perhaps with the development of peripheral technologies, new sensors will appear.

1. Intelligent driving technology is the future development trend

Intelligent driving technology is considered the future development direction of the automotive industry, including commercial vehicles, passenger cars, and low-speed driving in some specific scenarios. The development of intelligent driving technology is to improve road safety, reduce traffic accidents, and provide drivers and passengers with a more convenient and efficient travel experience. This is the vision for the development of this technology. The core of intelligent driving technology is that the vehicle can perceive the surrounding environment and make corresponding decisions and operations to achieve autonomous driving.

2. Multi-sensor fusion will be an inevitable choice in the application of intelligent driving:

Intelligent driving requires accurate and comprehensive perception of the surrounding environment to make correct decisions. Since different sensors have different working principles and adaptability, it is difficult for a single sensor to meet the needs of all scenarios. Therefore, multi-sensor fusion is an inevitable choice to achieve comprehensive perception and reliable decision-making. Multi-sensor fusion can comprehensively utilize information from different sensors to improve the accuracy and robustness of perception, thereby enhancing the safety of intelligent driving systems.

3. Millimeter-wave radar, LiDAR, and cameras have their own advantages and disadvantages. The core lies in how to choose and prioritize these devices in commercial applications:

Millimeter-wave radar: Millimeter-wave radar perceives surrounding objects by transmitting and receiving millimeter-wave signals, and has a long detection distance and stability under severe weather conditions. It can provide high-precision distance and speed information and is very effective for obstacle detection and tracking. However, millimeter-wave radar is relatively weak in object classification and detail perception.

LiDAR: LiDAR uses laser beams to scan the surrounding environment to obtain high-resolution 3D point cloud data. LiDAR can provide accurate target position and shape information, which is very advantageous for obstacle detection and road boundary identification. However, LiDAR performance is limited in bad weather, such as rain and snow, and its cost is relatively high.

Camera: The camera can capture images and video information of the surrounding environment, and has rich visual perception capabilities. Through computer vision algorithms, the camera can realize functions such as target detection and recognition, and lane line detection. Cameras are powerful in identifying and classifying objects and can provide rich visual information. However, the performance of the camera in conditions such as low light, bright light, and bad weather may be limited. 

In commercial applications, for the sensor selection and prioritization of intelligent driving systems, various factors need to be considered comprehensively, such as functional requirements, performance, and reliability requirements, cost and feasibility requirements, data fusion and algorithm support redundancy capabilities, etc. This is a gradual process that requires time support and practical experience to continuously review, optimize and upgrade, and perhaps it can truly achieve large-scale commercial applications.

Functional requirements: According to the application scenarios and functional requirements of the intelligent driving system, determine which perception capabilities are required, such as obstacle detection, lane keeping, pedestrian recognition, etc. Different sensors have different advantages in different functions and need to be selected according to specific needs.

Performance and Reliability: Evaluate the performance, accuracy, robustness, and reliability of various sensors. For example, consider how millimeter-wave radar performs in bad weather, and how cameras perform when lighting conditions change.

Cost and Feasibility: Consider sensor cost, feasibility, and scalability. LiDAR is generally more expensive, while cameras are affordable and widely used, but require more complex computer vision algorithms to support them.

Data fusion and algorithm support: Multi-sensor fusion requires corresponding data fusion algorithms and system support. Evaluate the difficulty of data fusion for various sensors and the corresponding algorithm development and implementation costs.

4. Perhaps new sensors will appear with the development of surrounding technologies.

As technology continues to evolve, new sensors or technologies may emerge. For example, further development of millimeter-wave radar and LiDAR may provide higher-resolution, longer-range perception capabilities. At the same time, advances in computer vision and deep learning technologies may also make cameras more widely used in intelligent driving.

In summary, sensor selection and prioritization for future intelligent driving systems will be a dynamic process that requires comprehensive consideration of technological progress and business needs.