融合路边雷达与联网车感知，提升复杂交通中车辆定位精度
Multi-Observer Vehicle Localization Case Study with Roadside Radar and Connected Vehicle Sensing
- 用雷达和联网车激光雷达进行目标级观测融合
- 雷达在完整激光雷达数据下提升有限，但低频共享仍有效
- 适合研究车联网融合定位的开发者与交通系统研究人员
在智能交通系统中，准确估计车辆位置至关重要，尤其在联网车与传统车辆共存的混合交通场景中。路边基础设施与联网车辆可提供同一交通场景的互补观测，但实际决策级融合证据仍有限。本文提出一种多观测源车辆定位框架，融合静态路边雷达与动态激光雷达联网车的紧凑目标级检测结果。基于芬兰赫尔辛基城市交叉口的真实数据，以独立仪器化的目标车辆作为参考轨迹，对比了两种基于扩展卡尔曼滤波的定位策略。分别评估了雷达与激光雷达性能，并在正常感知、激光雷达更新率降低、模拟遮挡及不同目标车辆运动状态条件下探索两种融合策略。结果表明，在激光雷达完全可用时，融合性能主要由激光雷达主导，雷达观测虽较不准确且一致性差，仅带来有限改进。然而，自适应扩展卡尔曼滤波（AEKF）相较纯激光雷达基线仍有小幅提升，且在较低更新率下联网车的目标级观测依然有用。这表明决策级融合并非对强单传感器基线的自动增益，而是依赖具体场景。我们已将数据集与实现代码开源至 GitHub，以支持后续研究。
原文摘要 · Abstract (English)
In modern intelligent transportation systems, it is essential to accurately estimate vehicle positions, especially in mixed traffic conditions where both connected and conventional vehicles coexist. Roadside infrastructure and connected vehicles can provide complementary observations of the same traffic scene, but real-world evidence on decision-level fusion between these sources remains limited. This paper proposes a multi-observer vehicle localization framework that fuses compact object-level detections from a static roadside radar and a dynamic LiDAR-equipped connected vehicle. We evaluate the framework with real-world data collected at an urban intersection in Helsinki, Finland, with a separately instrumented target vehicle used as the reference trajectory. Two extended Kalman filter based strategies for the localization task were benchmarked. The performance of the radar and LiDAR sensors were evaluated separately, and the two fusion strategies were explored under nominal sensing conditions, reduced LiDAR update rates, simulated LiDAR occlusions, and different target-vehicle motion states. The results show that, under full LiDAR availability, fusion performance is dominated by the LiDAR observations, while the less accurate and less consistent radar observations provide only limited additional improvement. Nevertheless, AEKF achieves small gains over the LiDAR-only baseline, and object-level connected vehicle observations remain useful when shared at reduced update rates. These findings indicate that decision-level fusion provides scenario-dependent benefits rather than automatic improvement over a strong single-sensor baseline. We release the dataset and implementation on Github to support further research: https://github.com/AppuriAalto/multi-observer-vehicle-tracking
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