面向智能网联车辆的轨迹预测模型

doi:10.13229/j.cnki.jdxbgxb.20231046

Abstract

Abstract:

In contrast to traditional single-vehicle intelligent autonomous driving systems， which can only make predictions about the future based on their own perception of the environment， intelligent connected autonomous driving systems have the capability to enhance predictions by incorporating additional dynamic information about the surrounding road environment through V2X technology. Building upon the foundation of single-vehicle intelligent trajectory prediction， a specialized encoder was employd to enable the trajectory prediction model to seamlessly fuse its own perceptual information with dynamic road data obtained via V2X communication. The experimental results on the CARLA simulation dataset demonstrate that using V2X technology to obtain dynamic information of the surrounding road environment can more accurately predict vehicle trajectories compared to trajectory prediction algorithms that do not use dynamic environment information.

Key words: computer application technology, autonomous driving, internet of vehicles, trajectory prediction

CLC Number:

TP399

Jian WANG,Chen-wei JIA. Trajectory prediction model for intelligent connected vehicle[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(6): 1963-1972.

Figures/Tables 14

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下标	含义
0	车道状态未知
1	车道允许通行（被绿灯控制）
2	车道被黄灯控制
3	车道不允许通行（被红灯控制）

下标	含义
0~21	车道类型
22~25	车道方向
26~36	左车道类型
37~47	右车道类型
48	是否在交叉路口

城市	描述	训练集采样数	验证集采样数
1	小型城市交通场景1	100 000	25 000
2	小型城市交通场景2	100 000	25 000
3	大型的城市地图，带有环岛和大型路口场景	100 000	25 000
4	小型城市交通场景，有一条“8字形”公路	100 000	25 000
5	方形网格城市交通场景	100 000	25 000
6	高速公路场景，有长的高速公路和许多出入口，包含密歇根式左转路口	100 000	25 000
7	乡村道路场景，道路狭窄，几乎没有红绿灯	100 000	25 000
8	大型城市的市中心场景	100 000	25 000

属性	nuScenes	Argoverse	DICP1M
预测时长/s	2	3	3
轨迹数量/条	4.3×10³	324×10³	1×10⁶
城市数量/个	2	2	8
采样频率/Hz	2	10	10
动态交通环境信息	-	-	√
总计时长/h	5.5	320	1 389

超参数	Argoverse	DICP1M
嵌入维度	128	128
学习率	5×10^-4	5×10^-4
训练轮次	64	64
随机失活概率	0.1	0.1
训练精度	bf16	bf16
批量大小	16	128
权重衰减率	3×10^-4	3×10^-4

Trajectory prediction model for intelligent connected vehicle

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数据集	minADE↓	minFDE↓	minMR↓
Argoverse¹	0.669 5	0.985 4	0.096 1
DICP1M¹	0.301 9	0.620 9	0.074 6
DICP1M²	0.281 3	0.571 4	0.067 4

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