Extended Object Tracking in Curvilinear Road Coordinates for Autonomous Driving
This work addresses the need for road-aligned state representation in autonomous driving subsystems like cruise control, but it is incremental as it adapts existing methods to a new coordinate system.
The paper tackled the problem of extended object tracking for autonomous driving by proposing a GM-PHD filter with a UKF estimator that provides obstacle state estimates in curvilinear road coordinates, validated through simulation and experimental data.
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.