TY - JOUR
T1 - 基于车辆视角数据的行人轨迹预测与风险等级评定
AU - Zhang, Zheyu
AU - Chao, Lü
AU - Li, Jinghang
AU - Xiong, Guangming
AU - Wu, Shaobin
AU - Gong, Jianwei
N1 - Publisher Copyright:
© 2022, Editorial Board, Journal of Applied Optics. All right reserved.
PY - 2022/5/25
Y1 - 2022/5/25
N2 - The commonly used pedestrian trajectory and risk prediction model based on roadbed-perspective data often cannot avoid complex modeling calculation and manual judgment. For succinctly and effectively predicting pedestrian trajectory and evaluating risk grade, a pedestrian trajectory and risk grade prediction model is created based on vehicle-perspective pedestrian data in this paper. The acquisition of vehicle-perspective pedestrian data, the prediction of pedestrian trajectory based on long-short term memory neural network and the assessment of risk grade based on clustering analysis - support vector machine method are successively conducted. The results of experiments show that the data-driven model built based on vehicle-perspective pedestrian data can capture the movement tendency and interaction characteristics of pedestrian and vehicle and is capable of predicting pedestrian trajectory and assessing risk grade.
AB - The commonly used pedestrian trajectory and risk prediction model based on roadbed-perspective data often cannot avoid complex modeling calculation and manual judgment. For succinctly and effectively predicting pedestrian trajectory and evaluating risk grade, a pedestrian trajectory and risk grade prediction model is created based on vehicle-perspective pedestrian data in this paper. The acquisition of vehicle-perspective pedestrian data, the prediction of pedestrian trajectory based on long-short term memory neural network and the assessment of risk grade based on clustering analysis - support vector machine method are successively conducted. The results of experiments show that the data-driven model built based on vehicle-perspective pedestrian data can capture the movement tendency and interaction characteristics of pedestrian and vehicle and is capable of predicting pedestrian trajectory and assessing risk grade.
KW - Clustering analysis
KW - Long short term memory neural network
KW - Pedestrian risk grade assessment
KW - Pedestrian trajectory prediction
KW - Support vector machine
KW - Vehicle-perspective pedestrian data
UR - http://www.scopus.com/inward/record.url?scp=85131175105&partnerID=8YFLogxK
U2 - 10.19562/j.chinasae.qcgc.2022.05.004
DO - 10.19562/j.chinasae.qcgc.2022.05.004
M3 - 文章
AN - SCOPUS:85131175105
SN - 1000-680X
VL - 44
SP - 675
EP - 683
JO - Qiche Gongcheng/Automotive Engineering
JF - Qiche Gongcheng/Automotive Engineering
IS - 5
ER -