{"ID":2850553,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.21278","arxiv_id":"2510.21278","title":"Track-to-Track Association for Collective Perception based on Stochastic Optimization","abstract":"Collective perception is a key aspect for autonomous driving in smart cities as it aims to combine the local environment models of multiple intelligent vehicles in order to overcome sensor limitations. A crucial part of multi-sensor fusion is track-to-track association. Previous works often suffer from high computational complexity or are based on heuristics. We propose an association algorithms based on stochastic optimization, which leverages a multidimensional likelihood incorporating the number of tracks and their spatial distribution and furthermore computes several association hypotheses. We demonstrate the effectiveness of our approach in Monte Carlo simulations and a realistic collective perception scenario computing high-likelihood associations in ambiguous settings.","short_abstract":"Collective perception is a key aspect for autonomous driving in smart cities as it aims to combine the local environment models of multiple intelligent vehicles in order to overcome sensor limitations. A crucial part of multi-sensor fusion is track-to-track association. Previous works often suffer from high computation...","url_abs":"https://arxiv.org/abs/2510.21278","url_pdf":"https://arxiv.org/pdf/2510.21278v1","authors":"[\"Laura M. Wolf\",\"Vincent Albert Wolff\",\"Simon Steuernagel\",\"Kolja Thormann\",\"Marcus Baum\"]","published":"2025-10-24T09:24:14Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.RO\"]","methods":"[]","has_code":false}