发表机构
North Dakota State University（北达科他州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对多交互车辆场景级变道意图及轨迹预测问题，提出动态场景图注意力框架，将场景表示为时变交互图，通过时态图注意力消息传递等捕捉车辆关系与线索，实验表明该框架能提升预测准确率，降低碰撞率和误差，验证了组件贡献和公式有效性。
AI 中文摘要
先进驾驶辅助系统和自动驾驶车辆中的安全运动规划需要准确理解周围交通场景的可能演变。然而，现有许多变道预测方法仍以单个目标车辆为中心，多智能体预测方法往往仅通过未来位置描述场景演变，提供的与每辆车机动相关的明确信息有限。本研究提出了一种动态场景图注意力框架，用于预测局部交通场景中每辆相关车辆的变道意图和未来轨迹。场景被表示为时变交互图，车辆被建模为节点，其空间和运动学关系通过显式边特征编码。时态图注意力消息传递捕捉车辆间不断演变的依赖关系和预机动线索，意图引导解码器将每个预测机动与其相应的未来运动联系起来。场景级一致性目标进一步鼓励兼容的多车辆未来。在NGSIM I-80、NGSIM US-101和highD数据集上的实验表明，与竞争基线相比有持续改进。DSiGAT在NGSIM I-80和US-101上的意图预测准确率分别达到90.12%和90.97%，相对于最强基线，轨迹RMSE降低了52.94%。它还产生了更低的智能体间碰撞率和联合位移误差，表明场景级预测更连贯。消融、敏感性、鲁棒性和定性分析进一步验证了所提出组件的贡献和以场景为重点的公式的有效性。
英文摘要
Safe motion planning in advanced driver-assistance systems and autonomous vehicles requires an accurate understanding of how the surrounding traffic scene is likely to evolve. However, many existing lane-change prediction methods remain centered on a single target vehicle, while multi-agent forecasting approaches often describe scene evolution only through future positions and provide limited explicit information about the maneuver associated with each vehicle. This study proposes a dynamic scene graph attention framework that predicts the lane-change intention and future trajectory of every relevant vehicle within a local traffic scene. The scene is represented as a time-varying interaction graph in which vehicles are modeled as nodes and their spatial and kinematic relationships are encoded through explicit edge features. Temporal graph-attention message passing captures evolving inter-vehicle dependencies and pre-maneuver cues, while an intention-guided decoder links each predicted maneuver to its corresponding future motion. A scene-level consistency objective further encourages compatible multi-vehicle futures. Experiments on the NGSIM I-80, NGSIM US-101, and highD datasets demonstrate consistent improvements over competing baselines. DSiGAT achieves intention prediction accuracies of 90.12% and 90.97% on NGSIM I-80 and US-101, respectively, and reduces trajectory RMSE by up to 52.94% relative to the strongest baseline. It also produces lower inter-agent collision rates and joint displacement errors, indicating more coherent scene-level predictions. Ablation, sensitivity, robustness, and qualitative analyses further validate the contribution of the proposed components and the effectiveness of the scene-focused formulation.
Comments28 pages, 9 Figures, 16 Tables
