论文标题
部分可观测时空混沌系统的无模型预测
Extended Object Tracking in Curvilinear Road Coordinates for Autonomous Driving
论文作者
论文摘要
在文献中，为自动驾驶而开发的扩展对象跟踪（EOT）算法主要在车辆参考框架中提供笛卡尔坐标的状态估计。但是，在许多情况下，在实施自主驾驶子系统等自主驾驶子系统（如巡航控制，巷道维护辅助，排等等）时，首选的状态表示是优选的。我们在LIDAR和雷达传感器之间采用混合传感器融合结构，以获得EOT的丰富测量点表示。通过使用Cutic Hermit Spline Road模型将UKF估算器的测量模型从曲线道路坐标到笛卡尔坐标的整合而开发。提出的算法通过MATLAB驱动方案设计器模拟和在Monza Eni电路收集的实验数据进行验证。
In literature, Extended Object Tracking (EOT) algorithms developed for autonomous driving predominantly provide obstacles state estimation in cartesian coordinates in the Vehicle Reference Frame. However, in many scenarios, state representation in road-aligned curvilinear coordinates is preferred when implementing autonomous driving subsystems like cruise control, lane-keeping assist, platooning, etc. This paper proposes a Gaussian Mixture Probability Hypothesis Density~(GM-PHD) filter with an Unscented Kalman Filter~(UKF) estimator that provides obstacle state estimates in curvilinear road coordinates. We employ a hybrid sensor fusion architecture between Lidar and Radar sensors to obtain rich measurement point representations for EOT. The measurement model for the UKF estimator is developed with the integration of coordinate conversion from curvilinear road coordinates to cartesian coordinates by using cubic hermit spline road model. The proposed algorithm is validated through Matlab Driving Scenario Designer simulation and experimental data collected at Monza Eni Circuit.
