Extended Object Tracking in Curvilinear Road Coordinates for Autonomous Driving

نویسندگان

چکیده

In literature, Extended Object Tracking (EOT) algorithms developed for autonomous driving predominantly provide obstacles state estimation in cartesian coordinates the Vehicle Reference Frame. However, many scenarios, representation road-aligned curvilinear is preferred when implementing 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 estimates road coordinates. We employ hybrid sensor fusion architecture between Lidar and Radar sensors to obtain rich measurement point representations EOT. The model UKF integration of coordinate conversion from by using cubic hermit spline model. proposed algorithm validated through Matlab Driving Scenario Designer simulation experimental data collected at Monza Eni Circuit. The Experimental Dataset will be made publicly available upon acceptance.

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ژورنال

عنوان ژورنال: IEEE transactions on intelligent vehicles

سال: 2023

ISSN: ['2379-8904', '2379-8858']

DOI: https://doi.org/10.1109/tiv.2022.3171593