用随机优化提升多车感知中轨迹关联的准确性与效率。
Track-to-Track Association for Collective Perception based on Stochastic Optimization
- 基于多维似然函数，融合轨迹数量与空间分布信息进行关联
- 在复杂场景下计算多个高置信度关联假设，提升模糊情况处理能力
- 适用于智能网联汽车集体感知，尤其适合高动态交通环境
集体感知是智慧城市自动驾驶的关键技术，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。多源感知中的轨迹关联是核心环节。以往方法常面临计算复杂度过高或依赖启发式规则的问题。本文提出一种基于随机优化的关联算法，利用融合轨迹数量与空间分布的多维似然函数，并能生成多个关联假设。在蒙特卡洛仿真和真实集体感知场景中验证了该方法的有效性，可在模糊环境中高效计算高似然关联。
原文摘要 · Abstract (English)
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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