Extended Object Tracking in Curvilinear Road Coordinates for Autonomous Driving

TL;DR

This paper introduces a novel EOT algorithm that estimates obstacle states in curvilinear road coordinates using a GM-PHD filter with UKF, integrating Lidar and Radar data for autonomous driving applications.

Contribution

It presents a new EOT method in curvilinear coordinates with sensor fusion and coordinate conversion, tailored for autonomous driving scenarios.

Findings

Validated through simulation and real-world data at Monza Eni Circuit.

Achieved accurate obstacle tracking in curvilinear coordinates.

Demonstrated effectiveness of hybrid sensor fusion architecture.

Abstract

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.