Joint Prediction for Kinematic Trajectories in Vehicle-Pedestrian-Mixed Scenes

Huikun Bi, Zhong Fang, Tianlu Mao, Zhaoqi Wang, Z. Deng
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引用次数: 28

Abstract

Trajectory prediction for objects is challenging and critical for various applications (e.g., autonomous driving, and anomaly detection). Most of the existing methods focus on homogeneous pedestrian trajectories prediction, where pedestrians are treated as particles without size. However, they fall short of handling crowded vehicle-pedestrian-mixed scenes directly since vehicles, limited with kinematics in reality, should be treated as rigid, non-particle objects ideally. In this paper, we tackle this problem using separate LSTMs for heterogeneous vehicles and pedestrians. Specifically, we use an oriented bounding box to represent each vehicle, calculated based on its position and orientation, to denote its kinematic trajectories. We then propose a framework called VP-LSTM to predict the kinematic trajectories of both vehicles and pedestrians simultaneously. In order to evaluate our model, a large dataset containing the trajectories of both vehicles and pedestrians in vehicle-pedestrian-mixed scenes is specially built. Through comparisons between our method with state-of-the-art approaches, we show the effectiveness and advantages of our method on kinematic trajectories prediction in vehicle-pedestrian-mixed scenes.
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车辆-行人-混合场景运动轨迹联合预测
物体的轨迹预测对于各种应用(例如,自动驾驶和异常检测)具有挑战性和关键性。现有的方法大多侧重于同质行人轨迹预测,将行人视为没有大小的粒子。然而,它们无法直接处理拥挤的车辆-行人混合场景,因为车辆在现实中受到运动学的限制,理想情况下应将其视为刚性的非粒子物体。在本文中,我们针对异构车辆和行人使用单独的lstm来解决这个问题。具体来说,我们使用一个定向的边界框来表示每个车辆,根据其位置和方向计算,以表示其运动轨迹。然后,我们提出了一个称为VP-LSTM的框架来同时预测车辆和行人的运动轨迹。为了评估我们的模型,专门建立了一个包含车辆和行人在车辆-行人混合场景中的轨迹的大型数据集。通过与现有方法的比较,我们展示了我们的方法在车辆-行人混合场景中运动轨迹预测的有效性和优势。
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