Design of Real-time Pedestrian Trajectory Prediction System based on Jetson Xavier

Quankai Liu, Haifeng Sang
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Abstract

This paper presents a vehicle-mounted real-time pedestrian trajectory prediction system based on the embedded device Jetson Xavier. It achieves low-cost real-time pedestrian trajectory prediction using only the front camera of the vehicle. Firstly, pedestrian detection and tracking are implemented based on YoLoV7, while also estimating pedestrian poses and optical flow to provide multiple information sequences for the trajectory prediction network. Secondly, the pedestrian trajectory algorithm from a driver's perspective is studied, and a trajectory prediction algorithm that considers pedestrian pose, optical flow, and trajectory information is proposed. A novel multi-information fusion network is designed to better integrate multiple features. The algorithm is tested on the JAAD and PIE datasets, and the displacement errors are reduced by 6.35% and 3.28%, respectively, compared to BiTraP. Finally, the algorithm is ported to the embedded device Xavier and installed on a simulated vehicle for testing. By predicting pedestrian future trajectories based on pedestrian detection, collisions can be avoided in advance, improving the safety of autonomous driving. The proposed system has significant practical value.
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基于Jetson Xavier的实时行人轨迹预测系统设计
提出了一种基于嵌入式设备Jetson Xavier的车载实时行人轨迹预测系统。仅使用车辆的前置摄像头即可实现低成本的实时行人轨迹预测。首先,基于YoLoV7实现行人检测与跟踪,同时对行人姿态和光流进行估计,为轨迹预测网络提供多个信息序列;其次,研究了驾驶员视角下的行人轨迹算法,提出了一种考虑行人姿态、光流和轨迹信息的轨迹预测算法。设计了一种新型的多信息融合网络,以更好地融合多种特征。该算法在JAAD和PIE数据集上进行了测试,与BiTraP相比,位移误差分别降低了6.35%和3.28%。最后,将该算法移植到嵌入式设备Xavier上,并安装在仿真车辆上进行测试。通过基于行人检测的行人未来轨迹预测,可以提前避免碰撞,提高自动驾驶的安全性。该系统具有重要的实用价值。
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