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群组感知的行人轨迹预测方法研究

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自动驾驶场景下,大多数方法没有对行人群体进行建模,这样会对道路交通的安全造成影响.因此,提出了一种针对群组感知的行人轨迹预测网络(GPCNet).具体来说,在组内,从个体层面学习行人之间的交互,考虑不同行人的偏好问题.在组间,从群体层面学习行人组间的交互,使用社会力模型考虑行人轨迹的碰撞问题.仿真结果表明,与常用的轨迹预测方法相比,GPCNet在ETH和UCY数据集上的性能提高了约75.4%.
Pedestrian trajectory prediction method based on group perception
Most methods do not model the pedestrian groups in autonomous driving,which will have an impact on road traffic safety.Therefore,a group perception pedestrian trajectory prediction network called GPCNet was proposed.Spe-cifically,in intra-group,the interaction between pedestrian was learned at the individual level and the preference issue of different pedestrian was considered.In inter-group,the interaction between pedestrian groups was learned at the group level and the collision issue of pedestrian trajectory was considered using the social force model.Simulation results dem-onstrate that GPCNet improves the performance on the ETH and UCY datasets by 75.4%compared to the commonly used trajectory prediction methods.

autonomous drivingtrajectory predictionpedestrian grouproad safety

王汝言、周玉蝶、吴大鹏、段昂、崔亚平、何鹏

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重庆邮电大学通信与信息工程学院,重庆 400065

先进网络与智能互联技术重庆市高校重点实验,重庆 400065

泛在感知与互联重庆市重点实验室,重庆 400065

自动驾驶 轨迹预测 行人群体 道路安全

2024

通信学报
中国通信学会

通信学报

CSTPCD北大核心
影响因子:1.265
ISSN:1000-436X
年,卷(期):2024.45(12)