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arXiv · 2510.21278

Track-to-Track Association for Collective Perception based on Stochastic Optimization

Laura M. Wolf·Vincent Albert Wolff·Simon Steuernagel·Kolja Thormann·Marcus Baum
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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.

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Laura M. Wolf, Vincent Albert Wolff, Simon Steuernagel, Kolja Thormann, Marcus Baum. 2025-10-24. Track-to-Track Association for Collective Perception based on Stochastic Optimization. https://arxiv.org/abs/2510.21278

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