For the evaluation of autonomous driving systems, this paper provides a new approach of generating reference data for multiple extended object tracking. In our approach, we apply a forward-backward smoother for objects with star-convex shapes based on the Labeled Multi-Bernoulli (LMB) Random Finite Set (RFS) and recursive Gaussian processes. We further propose to combine a robust birth policy with a backward filter to solve the conflict between robustness and completeness of tracking. Thereby, cluster candidates are evaluated based on a quality measure to only initialize objects from more reliable clusters in the forward pass. Missing states will then be recovered by the backward filter through post-processing the unassociated data after the smoothing process. Simulations and real-world experiments demonstrate superior performance of the proposed method in both cardinality and individual state estimation compared to naive LMB filter and smoother for extended objects.
«For the evaluation of autonomous driving systems, this paper provides a new approach of generating reference data for multiple extended object tracking. In our approach, we apply a forward-backward smoother for objects with star-convex shapes based on the Labeled Multi-Bernoulli (LMB) Random Finite Set (RFS) and recursive Gaussian processes. We further propose to combine a robust birth policy with a backward filter to solve the conflict between robustness and completeness of tracking. Thereby, c...
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