Multi-Observer Vehicle Localization Case Study with Roadside Radar and Connected Vehicle Sensing

基于路侧雷达与网联车辆感知的多观测者车辆定位案例研究

Abstract: 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.

摘要： 在现代智能交通系统中，准确估计车辆位置至关重要，尤其是在网联车辆与传统车辆共存的混合交通环境下。路侧基础设施和网联车辆可以为同一交通场景提供互补的观测数据，但关于这些数据源之间决策级融合的实际应用证据仍然有限。

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.

本文提出了一种多观测者车辆定位框架，该框架融合了来自静态路侧雷达和配备激光雷达（LiDAR）的动态网联车辆的紧凑型目标级检测结果。我们使用在芬兰赫尔辛基一个城市路口采集的真实世界数据对该框架进行了评估，并使用一辆单独配备仪器的目标车辆作为参考轨迹。

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.

我们对两种基于扩展卡尔曼滤波（EKF）的定位策略进行了基准测试。我们分别评估了雷达和激光雷达传感器的性能，并在标称传感条件、降低激光雷达更新频率、模拟激光雷达遮挡以及不同目标车辆运动状态下，对两种融合策略进行了探索。

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.

结果表明，在激光雷达完全可用的情况下，融合性能主要由激光雷达观测结果主导，而精度较低且一致性较差的雷达观测仅能提供有限的额外改进。尽管如此，AEKF 相比仅使用激光雷达的基准方案仍取得了小幅提升，且在降低更新频率的情况下，目标级网联车辆观测数据依然具有实用价值。

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.

这些发现表明，决策级融合提供的是依赖于具体场景的收益，而非在强大的单传感器基准之上自动实现性能提升。我们已在 Github 上发布了数据集和实现代码，以支持后续研究。