自動運転向け曲線道路座標系における拡張物体追跡
Extended Object Tracking in Curvilinear Road Coordinates for Autonomous Driving
LiDARとレーダーを融合し、道路に沿った曲線座標系で障害物の状態を推定するGM-PHDフィルタとUKF推定器を提案し、シミュレーションと実車データで検証した。
著者: Pragyan Dahal, Simone Mentasti, Stefano Arrigoni, Francesco Braghin, Matteo Matteucci, Federico Cheli
分類: cs.RO
原文アブストラクト
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.
