Kalman-filter-based sensor fusion applied to road-objects detection and tracking for autonomous vehicles
Kalman-filter-based sensor fusion applied to road-objects detection and tracking for autonomous vehicles
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DOI：
10.1177/0959651820975523
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发表时间：
2021-08-01
影响因子：
1.6
通讯作者：
Farag, Wael
中科院分区：
文献类型：
--
作者：
Farag, Wael
本文提出了一种用于自动驾驶的实时道路目标检测与跟踪（LR_ODT）方法。该方法基于激光雷达和雷达测量数据的融合，它们安装在汽车上，并采用定制的Unscented卡尔曼滤波器进行数据融合。这两种设备的优点相结合，使用所提出的融合方法，精确地提供姿态和速度信息的对象在道路上移动的自我汽车周围。与其他检测和跟踪方法不同，平衡处理姿态估计精度和实时性能是这项工作的主要贡献。该技术使用高性能语言C++实现，并利用高度优化的数学和优化库实现最佳实时性能。仿真研究已经进行了评估的LR_ODT跟踪自行车，汽车和行人的性能。最后，将Unscented卡尔曼滤波融合与扩展卡尔曼滤波融合的性能进行了比较，说明了Unscented卡尔曼滤波融合的优越性。Unscented卡尔曼滤波器在所有测试用例和所有状态变量水平上都优于扩展卡尔曼滤波器（平均均方根误差为-24%）。所采用的融合技术表明，与使用单个设备相比，跟踪性能的改善是多么突出（激光雷达的均方根误差为-29%，雷达的均方根误差为-38%）。
In this article, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. This method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the Unscented Kalman Filter fusion is compared to that of the Extended Kalman Filter fusion showing its superiority. The Unscented Kalman Filter has outperformed the Extended Kalman Filter on all test cases and all the state variable levels (-24% average Root Mean Squared Error). The employed fusion technique shows how outstanding is the improvement in tracking performance compared to the use of a single device (-29% Root Mean Squared Error with lidar and -38% Root Mean Squared Error with radar).