基于路侧雷达与网联车辆感知的多观测器车辆定位案例研究
Multi-Observer Vehicle Localization Case Study with Roadside Radar and Connected Vehicle Sensing
- Aalto University（阿尔托大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出融合路侧雷达与网联车辆感知的多观测器车辆定位框架，经实测数据验证，其性能受场景影响，AEKF可小幅优于仅用LiDAR的基线，相关成果与代码已开源。
AI中文摘要：
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常规车辆共存的混合交通场景下。路侧基础设施与网联车辆可提供同一交通场景的互补观测，但关于这些数据源间决策级融合的实际证据仍较为有限。本文提出一种多观测器车辆定位框架，该框架融合静态路侧雷达与搭载激光雷达（LiDAR）的动态网联车辆输出的紧凑目标级检测结果。我们利用在芬兰赫尔辛基某城市交叉口采集的实际数据对该框架进行评估，其中采用单独配备设备的目标车辆作为参考轨迹。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，分别评估了雷达与LiDAR传感器的性能，并在标称感知条件、LiDAR更新率降低、模拟LiDAR遮挡以及不同目标车辆运动状态下探究了两种融合策略。结果表明，在LiDAR完全可用的情况下，融合性能由LiDAR观测主导，而精度较低、一致性较差的雷达观测仅能提供有限的额外提升；尽管如此，自适应扩展卡尔曼滤波（AEKF）相较于仅用LiDAR的基线仍实现了小幅增益，且当目标级网联车辆观测以降低后的更新率共享时仍具实用性。这些发现表明，决策级融合的益处取决于具体场景，而非必然优于性能强劲的单传感器基线。我们在Github上发布了数据集与实现代码以支持进一步研究：https://this.url
英文摘要：
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
