单车智能条件下综合车辆运动状态的轨迹预测方法

doi:10.13229/j.cnki.jdxbgxb.20250495

Abstract

Abstract:

To achieve accurate prediction of surrounding vehicles' future trajectories for autonomous vehicles under single-vehicle intelligence， a trajectory prediction method integrating vehicle perception bounding boxes and motion states is proposed. By fusing data from onboard camera images， LiDAR， and ego-vehicle motion information， the proposed method extensively extracts the motion features of target vehicles and the motion trends of the ego vehicle. First， the target vehicle's trajectory and displacement are encoded， and a cross-modal attention mechanism with visual feature fusion is established. Second， an ego-vehicle displacement-difference/angle-difference encoder is designed； combined with optical flow features， it accurately captures the ego vehicle's motion trend， and a Transformer architecture is employed to achieve deep fusion of the target vehicle's motion features with the ego vehicle's motion features. Finally， the fused features， together with the target vehicle's optical flow features， are fed into a decoder to generate the predicted trajectory of the target vehicle. The proposed trajectory prediction model was trained and tested on the NuScenes dataset and deployed on a modified Dongfeng AX7 intelligent vehicle for real-vehicle experiments. The results demonstrate that the proposed method has advantages in both prediction accuracy and deployment capability.

Key words: vehicle trajectory prediction, LiDAR, visual features, optical flow information, multimodal fusion

CLC Number:

U495

Hong-yan GUO,Hong-nan XIE,Qing-yu MENG. Trajectory prediction method of integrated vehicle motion states under single-vehicle intelligence conditions[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(10): 2535-2545.

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References 24

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模型	minADE₅	minADE₁₀		minFDE₁₀
本文方法	2.02	1.75	3.86	3.12
DSF?AF	2.06	1.66	4.67	3.71
DLow?AF	2.11	1.78	4.70	3.58
MID	2.15	1.85	4.70	3.90

硬件名称	参数介绍
车载计算单元	型号为Nuvo?6108GC；CPU为Intel Xeon E3?1275 v5，4 核 8 线程，主频 3.6 GHz；GPU为NVIDIA GeForce RTX 2080，8 GB GDDR6；内存为32 GB；操作系统为Ubuntu 18.04 LTS
摄像头	5 000 万像素；Sony IMX800 / 光影猎人900，f/1.6，等效 23 mm，OIS，8K@24 fps，4K@60/30 fps；支持 OIS + EIS
激光雷达	32 线机械旋转式；激光波长为905 nm；激光等级为Class 1；测距精度为±3 cm；测距范围为0.4 m~200 m，目标反射率为20 %；视场角为360°× 40°；输出三维空间坐标、反射强度

工况	平均位移误差（ADE）/m
工况	0.5 s	1.0 s	1.5 s	2.0 s	2.5 s	3.0 s
直行	0.29	0.40	0.67	0.82	0.84	0.80
转向	0.64	0.89	1.07	1.20	1.27	1.28

工况	最终位移误差（FDE）/m
工况	0.5 s	1.0 s	1.5 s	2.0 s	2.5 s	3.0 s
直行	0.29	0.51	1.21	1.28	0.94	0.56
转向	0.64	1.14	1.42	1.58	1.57	1.31

Trajectory prediction method of integrated vehicle motion states under single-vehicle intelligence conditions

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