Automotive LiDAR’s Role in Autonomous Driving

With the development of automation technology and smart cities, autonomous driving becomes increasingly known by the public. In near future, it is a tendency to build an intelligent network in which autonomous driving plays an important role.

Challenges in autonomous driving

However, autonomous driving is now in the stage of L2 ADAS or below. There are still some main problems the industry is facing:

1. Autonomous driving-related technology develops faster than regulations. However, regulations encourage but restrict its development.
2. Safety accidents frequently occur due to unmatured autonomous driving technology. Psychologically, consumers still have fears and doubts about autonomous driving technology.
3. Autonomous driving highly depends on sensors, processors, and algorithms, HD maps, etc. LiDAR, millimeter-wave radar and camera are three key sensor technologies for autonomous driving. Among them, LiDAR wins more attention. However, LiDARs needed for autonomous vehicles is currently facing technical and cost challenges.

Why is LiDAR important in self-driving?

After several safety accidents occurred in Tesla self-driving cars with cameras as sensors, the industry has become alarmed that unmanned vehicles cannot completely depend on the camera as the eyes, but still needs the escort of automotive LiDAR sensors.

What problems does LiDAR solve?

1. The most significant advantage of LiDAR is that it can be used normally no matter at night or at day, while the camera does not function well at night. 
2. The most important problem is that when the visual computing power is insufficient in the situation of a visual corner (various boundaries), automotive LiDAR can continue to support autonomous driving and construct three-dimensional images, so as to improve the safety of autonomous driving.
3. By constructing a three-dimensional environment, automotive LiDAR can avoid the fatal weakness in millimeter-wave radar which filters the static objects or fails to detect large numbers of small targets on the road, that cause the car crashed into the obstacle.

Working principle

In autonomous driving, automotive LiDAR can calculate the relative distance between the target and itself according to the turn-back time after the laser encounters an obstacle. The laser beam can accurately measure the relative distance between the LiDAR device and the edge of the contour of the object in the field of view. This contour information forms a so-called point cloud and constructs a 3D environment map with an accuracy of centimeters, thereby to improve the measurement accuracy.

Neuvition LiDAR recommended for autonomous driving:

Titan M1-Pro

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Features of Titan M1-Pro: