Automotive LiDAR Key Information

Author: Neuvition, IncRelease time:2021-08-16 03:19:50

Among all automotive sensor types dedicated to enhancing sensing and object detection capabilities, automotive LiDAR is by far the most complex and diverse choice. What key information about automotive LiDAR you should know?

automotive lidar

1. Key parameters of automotive LiDAR

1) Field of view, including horizontal FOV and vertical FOV

2) Resolution, including horizontal resolution and vertical resolution

a. It is not difficult to achieve high resolution in the horizontal direction, because the horizontal resolution is driven by a motor, so the horizontal resolution can be made very high. At present, the products of domestic and foreign LiDAR manufacturers have a horizontal resolution of 0.1 degree.

b. The vertical resolution is related to the geometric size of the transmitter and also to its arrangement, that is, the smaller the distance between two adjacent transmitters is, the smaller the vertical resolution will be.

3) Ranging range

4) Distance accuracy

5) Refresh frequency

6) Scanning frequency-how many scans the LiDAR has done in 1s

7) Laser wavelength-currently the more common ones are 905nm and 1550nm

2. Interpretation of characteristics of automotive LiDAR

The role of LiDAR and camera in autonomous driving is relatively similar. From a certain perspective, LiDAR can also be regarded as a visual sensor; but compared with cameras, LiDAR also has its unique advantages:

1) Completely eliminate the interference of light

Whether it is day or night, whether it is an avenue with mottled shadows or a tunnel exit with ever-changing light, it will not interfere with the LiDAR;

2) LiDAR can easily obtain three-dimensional information, while the camera cannot process 3D data;

3) The effective range of the LiDAR is farther than that of the camera.

Example: The current LKA function generally requires that the vehicle speed is above 60~70km/h to work normally. Because the camera visual sampling points are insufficient at low speeds, the accuracy of the lane line fitting is low. While the effective distance of the LiDAR is generally 4-5 times that of the camera visual system. There are more effective sampling points. When the vehicle speed is low, the lane line detection accuracy is much higher than that of the camera visual system;

automotive lidar -neuvition

4) LiDAR can solve the problem of short-range lateral vision blind area;

5) When the vehicle is driving at a low speed, the LiDAR is better than the camera in terms of target recognition and classification;

6) Point cloud conversion requires low computing power; higher density drawing can be performed directly through the point cloud.

3. Challenges and development direction of automotive LiDAR applications

3.1 Challenges

1) The high cost is the biggest obstacle to the large-scale promotion and use of automotive LiDAR.

2) Difficulties in mass production at automotive LiDAR: needs to meet various requirements such as performance, environmental adaptability, reliability, and product consistency in order to achieve vehicle-level mass production, and suppliers need to establish standardized and automated assembly production lines. In addition, the effective verification method for automotive LiDAR vehicle level has not yet been concluded.

3) The climate environment affects the detection beam of the automotive LiDAR, and the beam is affected by the effects of atmospheric absorption, scattering, and refraction.

a. The LiDAR in smart driving cars is generally installed on the top of the car or embedded around the car body. The lower installation height makes the echo reduction effect caused by certain gas molecules and suspended particles in the atmosphere larger, resulting in a worse reception effect of the LiDAR detector.

b. In severe weathers such as rain, fog, ice, and snow, suspended objects in the air will have an adverse effect on the laser emission, reflection, and detection processes, resulting in a reduction in the detection range and detection accuracy of the LiDAR.

3.2 Development Trend

1) It is a trend that solid-state LiDAR will be widely used, which can reduce costs and meet the needs of vehicles.

2) Automotive LiDAR is becoming increasingly intelligent

 Automotive LiDAR may be used as a node in the entire smart driving network, not only dedicated to smart cars, but also reasonably responding to network terminal commands to adjust its own working mode, to achieve software and hardware decoupling, and complete sensing tasks more efficiently and flexibly;

3) Multi-sensor data fusion

Multi-sensor redundant configuration and information fusion will break through the limitations of a single sensor, give play to the combined advantages of multiple sensors, improve system reliability and robustness, expand the system’s time and space coverage, and more accurately and comprehensively perceive the environment.

4) Optimization and packaging of automotive LiDAR algorithms

The complexity and diversity of intelligent driving scenarios have caused the diversity and specificity of LiDAR application algorithms. In order to facilitate transplantation and improve development efficiency, the optimization and packaging of the typical algorithms can serve as mature modules for developers to explore more.

MEMS+1550nm laser is currently the only mature technical solution that can pass the vehicle-level certification, with lower cost and higher accuracy. Neuvition LiDAR has been focusing on the solid-state MEMS+1550nm technology and is one of the most advanced commercially vehicle LiDARs available in the market with many parameters ahead of the competition.

automotive lidar neuvition

Neuvition’s solid-state HD vehicle LiDAR uses MEMS micro-galvanometer plus 1550nm laser technology, which improves reliability but cuts cost. Titan M1-Pro LiDAR has a high resolution of 480 lines, a FOV of 120 °, and an effective detection distance of 200 meters. The super performance and significant improvement on safety for autonomous driving make Titan M1-Pro the best solid-state vehicle LiDAR for self-driving cars.