Real-Time Motion Classification of LiDAR Point Detection for Automated Vehicles

A Light Detection And Ranging (LiDAR) is now becoming an essential sensor for an autonomous vehicle. The LiDAR provides the surrounding environment information of the vehicle in the form of a point cloud. A decision-making system of the autonomous car is able to determine a safe and comfort maneuver by utilizing the detected LiDAR point cloud. The LiDAR points on the cloud are classified as dynamic or static class depending on the movement of the object being detected. If the movement class (dynamic or static) of detected points can be provided by LiDAR, the decision-making system is able to plan the appropriate motion of the autonomous vehicle according to the movement of the object. This paper proposes a real-time process to segment the motion states of LiDAR points. The basic principle of the classification algorithm is to classify the point-wise movement of a target point cloud through the other point clouds and sensor poses. First, a fixed size buffer store the LiDAR point clouds and sensor poses for a constant time window. Second, motion beliefs of the target point cloud against other point clouds and sensor poses in the buffer are estimated, respectively. Each motion belief of the points in the target point cloud is represented by a series of masses of dynamic, static, and unknown based on the evidence theory. Finally, the series of motion belief masses of the target point cloud for the other point clouds and poses are integrated through the Dempster-Shafer combination. The integrated mass value is used to classify the point-wise motion of the target point cloud into the state of dynamic, static, and unknown. The proposed algorithm was quantitatively evaluated through the simulation of LiDAR sensors and surrounding environment. Then, the algorithm was qualitatively validated through the experiments using an autonomous car equipped with LiDAR. The autonomous vehicle was able to perform the 3D point cloud mapping and map-matching localization.

• Record URL:
  https://doi.org/10.4271/2020-01-0703
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Jo, Kichun
  • Kim, Chansoo
  • Cho, Sungjin
  • Sunwoo, Myoungho
• Conference:
  • WCX SAE World Congress Experience
  • Location: Detroit Michigan, United States
  • Date: 2020-4-21 to 2020-4-23
• Publication Date: 2020-4-14

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • SAE Technical Paper
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Autonomous land vehicles; Detection and identification; Intelligent vehicles; Laser radar; Mathematical models; Real time information; Sensors
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01740199
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2020-01-0703
• Files: TRIS, SAE
• Created Date: May 26 2020 10:20AM