Three-Dimensional LiDAR Decoder Design for Autonomous Vehicles in Smart Cities

Yu-Cheng Fan,Sheng-Bi Wang

doi:10.3390/info13010018

Yu-Cheng Fan, Sheng-Bi Wang

Open Access

https://doi.org/10.3390/info13010018

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Abstract

With the advancement of artificial intelligence, deep learning technology is applied in many fields. The autonomous car system is one of the most important application areas of artificial intelligence. LiDAR (Light Detection and Ranging) is one of the most critical components of self-driving cars. LiDAR can quickly scan the environment to obtain a large amount of high-precision three-dimensional depth information. Self-driving cars use LiDAR to reconstruct the three-dimensional environment. The autonomous car system can identify various situations in the vicinity through the information provided by LiDAR and choose a safer route. This paper is based on Velodyne HDL-64 LiDAR to decode data packets of LiDAR. The decoder we designed converts the information of the original data packet into X, Y, and Z point cloud data so that the autonomous vehicle can use the decoded information to reconstruct the three-dimensional environment and perform object detection and object classification. In order to prove the performance of the proposed LiDAR decoder, we use the standard original packets used for the comparison of experimental data, which are all taken from the Map GMU (George Mason University). The average decoding time of a frame is 7.678 milliseconds. Compared to other methods, the proposed LiDAR decoder has higher decoding speed and efficiency.

Highlights

LiDAR [1,2,3,4,5] mainly uses ultraviolet light, visible light, or near-infrared light as the ray medium
As LiDAR is widely used, it is divided into Airborne LiDAR [6,7,8], Terrestrial LiDAR [9,10,11,12,13,14,15,16,17,18], Bathymetric LiDAR [19,20,21,22], and Mobile LiDAR, according to data specifications, data purpose, measurement range, etc. [1]
We can see that the greater the number of packets, the longer the decoding time, which increases proportionally, and it is much longer than the point cloud image reconstruction time

Summary

Introduction

LiDAR [1,2,3,4,5] mainly uses ultraviolet light, visible light, or near-infrared light as the ray medium. Objects of different materials can be scanned, such as rocks, rain, chemical objects, smoke, clouds, etc. We first discuss the different types of LiDAR and their applications and discuss the Velodyne (San Jose, CA, USA) HDL-64 LiDAR used, including its hardware specifications and characteristics, as well as the package structure [1]. The Mobile LiDAR [23,24,25,26] is mainly installed on vehicles. The most common one is Google self-driving cars. Since 2009, Google’s Waymo company has begun to conduct research and development and testing of self-driving cars. The more common autopilot device is Velodyne’s HDL-64 LiDAR. The most important thing about LiDAR is that it can capture 360-degree surrounding scene data, that is, depth information such as distance, which is difficult for ordinary sensors [1]

Results

Discussion

Conclusion