标题： 数据驱动的目标跟踪：将模块化神经网络集成到卡尔曼框架中 显示英文标题

标题： Data-Driven Object Tracking: Integrating Modular Neural Networks into a Kalman Framework

摘要： 本文提出了针对多目标跟踪（MOT）的新颖机器学习（ML）方法，专门设计以满足先进驾驶辅助系统（ADAS）日益增长的复杂性和精度需求。我们介绍了三种神经网络（NN）模型来解决MOT中的关键挑战：(i) 单预测网络（SPENT），用于轨迹预测，(ii) 单关联网络（SANT），用于将单个传感器对象（SO）映射到现有轨迹，以及(iii) 多关联网络（MANTa），用于将多个SO关联到多个轨迹。这些模型无缝集成到传统的卡尔曼滤波器（KF）框架中，通过替换相关组件保持系统的模块化，而不破坏整体架构。重要的是，所有三个网络都设计为在实时嵌入式环境中运行。每个网络包含少于50,000个可训练参数。我们在公开的KITTI跟踪数据集上的评估表明，跟踪性能有显著提升。与标准KF相比，SPENT将均方根误差（RMSE）减少了50%，而SANT和MANTa在传感器对象到轨迹的分配上达到了高达95%的准确率。这些结果强调了将任务特定的NN集成到传统跟踪系统中的有效性，提高了性能和鲁棒性，同时保留了模块化、可维护性和可解释性。 显示英文摘要

摘要： This paper presents novel Machine Learning (ML) methodologies for Multi-Object Tracking (MOT), specifically designed to meet the increasing complexity and precision demands of Advanced Driver Assistance Systems (ADAS). We introduce three Neural Network (NN) models that address key challenges in MOT: (i) the Single-Prediction Network (SPENT) for trajectory prediction, (ii) the Single-Association Network (SANT) for mapping individual Sensor Object (SO) to existing tracks, and (iii) the Multi-Association Network (MANTa) for associating multiple SOs to multiple tracks. These models are seamlessly integrated into a traditional Kalman Filter (KF) framework, maintaining the system's modularity by replacing relevant components without disrupting the overall architecture. Importantly, all three networks are designed to be run in a realtime, embedded environment. Each network contains less than 50k trainable parameters. Our evaluation, conducted on the public KITTI tracking dataset, demonstrates significant improvements in tracking performance. SPENT reduces the Root Mean Square Error (RMSE) by 50% compared to a standard KF, while SANT and MANTa achieve up to 95% accuracy in sensor object-to-track assignments. These results underscore the effectiveness of incorporating task-specific NNs into traditional tracking systems, boosting performance and robustness while preserving modularity, maintainability, and interpretability.