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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.21278 |
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| _version_ | 1866912668706668544 |
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| author | Wolf, Laura M. Wolff, Vincent Albert Steuernagel, Simon Thormann, Kolja Baum, Marcus |
| author_facet | Wolf, Laura M. Wolff, Vincent Albert Steuernagel, Simon Thormann, Kolja Baum, Marcus |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21278 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Track-to-Track Association for Collective Perception based on Stochastic Optimization Wolf, Laura M. Wolff, Vincent Albert Steuernagel, Simon Thormann, Kolja Baum, Marcus Signal Processing Robotics 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. |
| title | Track-to-Track Association for Collective Perception based on Stochastic Optimization |
| topic | Signal Processing Robotics |
| url | https://arxiv.org/abs/2510.21278 |