|Table of Contents|

Perception of moving objects in traffic scenes based on heterogeneous graph learning(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2022年03期
Page:
238-250
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Perception of moving objects in traffic scenes based on heterogeneous graph learning
Author(s):
YANG Biao1; 2;  YAN Guo-cheng1;  LIU Zhan-wen3;  LIU Xiao-feng2
(1. School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213016, Jiangsu, China; 2. College of Internet of Things Engineering, Hohai University, Changzhou 213003, Jiangsu, China; 3. School of Information, Chang'an University, Xi'an 710064, Shaanxi, China)
Keywords:
trajectory prediction;  traffic scene perception;  heterogeneous graph learning;  deep neural network;  object detection;  object tracking
PACS:
U491.1
DOI:
10.19818/j.cnki.1671-1637.2022.03.019
Abstract:
In order to improve the operation efficiency and transportation safety of unmanned vehicles in traffic scenes, the perception of moving objects in traffic scenes was investigated based on the heterogeneous graph learning.In view of the influence of complex interaction relations between moving objects on their motions in actual traffic scenes, an integrated perception framework of multi-object detection-tracking-prediction was proposed based on the heterogeneous graph learning. YOLOv5 and DeepSORT were combined to detect and track the moving objects, and the trajectories of the objects were obtained. The long short-term memory(LSTM)network was used to learn the objects' motion information from their historical trajectories, and a heterogeneous graph was introduced to learn the interaction information between the objects and improve the prediction accuracies of the trajectories of moving objects. The LSTM network was also utilized to decode the objects' motion and interaction information to obtain their future trajectories, and the method was evaluated on the public transportation datasets Argoverse, Apollo, and NuScenes to verify its effectiveness.Analysis results show that the combination of YOLOv5 and DeepSORT can realize the detection and tracking of moving objects and achieve a detection accuracy rate of 75.4% and a continuous tracking rate of 61.4% for moving objects in traffic scenes. The heterogeneous graph can effectively capture the complex interaction relations between moving objects, and the captured interaction relations can improve the accuracy of trajectory prediction. The error of the predicted average displacement of moving objects reduces by 63.0% after the interaction relations captured by the heterogeneous graph are added. As a result, it is effective to consider the interaction relations between moving objects in traffic scenes. The historical and future motion information of moving objects can be perceived by introducing the heterogeneous graph to capture the interaction relations between moving objects, so as to facilitate unmanned vehicles to better understand complex traffic scenes. 4 tabs, 9 figs, 36 refs.

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Last Update: 2022-07-20