论文标题
聚类车辆运动轨迹的通用框架
A Generic Framework for Clustering Vehicle Motion Trajectories
论文作者
论文摘要
自动驾驶汽车的开发需要在驾驶方案中访问大量数据。但是，此类驾驶场景的手动注释是昂贵的，并且要遵守基于规则的轨迹标签系统中的错误。 To address this issue, we propose an effective non-parametric trajectory clustering framework consisting of five stages: (1) aligning trajectories and quantifying their pairwise temporal dissimilarities, (2) embedding the trajectory-based dissimilarities into a vector space, (3) extracting transitive relations, (4) embedding the transitive relations into a new vector space, and (5) clustering the trajectories with an optimal number of集群。我们在由带注释的轨迹组成的具有挑战性的现实世界数据集上调查并评估所提出的框架。我们观察到，尽管轨迹长度不同，但提出的框架仍取得了令人鼓舞的结果。此外，我们扩展了框架以验证由生成对抗网络（GAN）生成的合成数据验证真实数据集的增强，我们检查生成的轨迹是否与真实的基础群集一致。
The development of autonomous vehicles requires having access to a large amount of data in the concerning driving scenarios. However, manual annotation of such driving scenarios is costly and subject to the errors in the rule-based trajectory labeling systems. To address this issue, we propose an effective non-parametric trajectory clustering framework consisting of five stages: (1) aligning trajectories and quantifying their pairwise temporal dissimilarities, (2) embedding the trajectory-based dissimilarities into a vector space, (3) extracting transitive relations, (4) embedding the transitive relations into a new vector space, and (5) clustering the trajectories with an optimal number of clusters. We investigate and evaluate the proposed framework on a challenging real-world dataset consisting of annotated trajectories. We observe that the proposed framework achieves promising results, despite the complexity caused by having trajectories of varying length. Furthermore, we extend the framework to validate the augmentation of the real dataset with synthetic data generated by a Generative Adversarial Network (GAN) where we examine whether the generated trajectories are consistent with the true underlying clusters.
