Abstract
Accurate and safety-conscious trajectory prediction is a key technology for intelligent transportation systems, especially in V2X-enabled urban environments with complex multi-agent interactions. In this paper, we created a digital twin-driven V2X trajectory prediction pipeline that jointly leverages cooperative perception from vehicles and infrastructure to forecast multi-agent motion at signalized intersections. The proposed model combines a Bi-LSTM-based generator with a structured training objective consisting of a standard mean squared error (MSE) loss and a novel twin loss. The twin loss encodes infrastructure constraints, collision avoidance, diversity across predicted modes, and rule-based priors derived from the digital twin. While the MSE term ensures point-wise accuracy, the twin loss penalizes traffic rule violations, predicted collisions, and mode collapse, guiding the model toward scene-consistent and safety-compliant predictions. We train and evaluate our approach on real-world V2X data sent from the intersection to the vehicle and collected in urban corridors. In addition to standard trajectory metrics (ADE, FDE), we introduce ITS-relevant safety indicators, including infrastructure and rule violation rates. Experimental results demonstrate that the proposed training scheme significantly reduces critical violations while maintaining comparable prediction accuracy and real-time performance, highlighting the potential of digital twin-driven multi-loss learning for V2X-enabled intelligent transportation systems.
Abstract (translated)
准确且注重安全的轨迹预测是智能交通系统的关键技术，特别是在复杂多代理交互的V2X（车联网）增强型城市环境中。本文中，我们创建了一个数字孪生驱动的V2X轨迹预测管道，该管道结合了来自车辆和基础设施的合作感知能力，以在有信号控制的交叉路口处预测多重代理的动作。所提出的模型结合了一个基于Bi-LSTM（双向长短期记忆网络）的生成器与一个包含标准均方误差(MSE)损失及新颖孪生损失在内的结构化训练目标。该孪生损失编码了基础设施约束、碰撞避免策略、不同预测模式间的多样性以及从数字孪生中推导出的行为准则。虽然MSE项保证了点对点精度，但孪生损失惩罚违反交通规则的预测行为、预计碰撞和模式崩溃，从而引导模型做出与场景一致且符合安全规范的预测。 我们在来自城市走廊中的交叉路口传输至车辆的真实世界V2X数据上训练并评估我们的方法。除标准轨迹指标（ADE-平均位移误差、FDE-最终位移误差）外，我们还引入了适用于ITS(智能交通系统)的安全指标，包括基础设施和规则违规率。实验结果表明，所提出的培训方案在保持相当的预测准确性和实时性能的同时，显著减少了关键性的违规行为，突显了数字孪生驱动多损失学习方法在V2X增强型智能交通系统中的潜力。
URL
https://arxiv.org/abs/2603.05546
