发表机构
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出PR-IMM，将基于Transformer的雷达多普勒预测模型集成到IMM中，以提升非线性运动表示，实验表明其位置误差较IMM降低57.3%，ID切换减少25.3%，IDF1提升9.6%。
AI 中文摘要
目标跟踪对于自动驾驶车辆避开障碍物和规划路线至关重要。雷达即使在恶劣天气下也能保持检测性能，并可通过多普勒效应测量相对速度，因此非常适合目标跟踪。本文提出了一种基于数据驱动的状态预测器的交互式多模型跟踪方法（PR-IMM），该方法在保持基于物理的运动模型的稳定性和可解释性的同时，改进了非线性物体运动表示。所提方法采用基于Transformer的预测模型（PR），该模型结合雷达多普勒测量来预测物体位移。PR模型作为模式与CV、CA和CT运动模型一起集成到IMM中，并根据模式概率动态组合它们的先验位置。实验结果表明，与IMM相比，PR-IMM将位置估计误差降低了57.3%，与PR相比降低了16.5%，同时将ID切换减少了25.3%，并将IDF1提高了9.6%。
英文摘要
Object tracking is essential for autonomous vehicles to avoid obstacles and plan routes. Radar maintains detection performance even in adverse weather and can measure relative velocity through the Doppler effect, making it well suited for object tracking. In this paper, we propose a data-driven state PRedictor-based Interacting Multiple Model tracking method (PR-IMM) that improves nonlinear object-motion representation while preserving the stability and interpretability of physics-based motion models. The proposed method employs a transformer-based PRediction model (PR) that incorporates radar Doppler measurements to predict object displacement. The PR model is integrated into the IMM as a mode alongside the CV, CA, and CT motion models, and their prior positions are dynamically combined according to the mode probabilities. Experimental results show that PR-IMM reduces position-estimation error by 57.3% over the IMM and by 16.5% over the PR, while reducing ID switches by 25.3% and improving IDF1 by 9.6% over the IMM.
Comments8 pages, 4 figures
