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.

翻译：集体感知是智慧城市中自动驾驶的关键环节，旨在融合多辆智能车辆的局部环境模型以克服传感器局限。多传感器融合的核心在于航迹关联。现有方法常面临计算复杂度高或依赖启发式规则的局限。本文提出一种基于随机优化的关联算法，该算法利用包含航迹数量及其空间分布的多维似然函数，并计算多种关联假设。通过蒙特卡洛仿真和真实集体感知场景的实验验证，本方法能在模糊环境下高效计算高似然关联结果。