A Generic Framework for Clustering Vehicle Motion Trajectories

TL;DR

This paper introduces a comprehensive non-parametric clustering framework for vehicle motion trajectories, addressing challenges of varying trajectory lengths and dataset annotation costs, with extensions involving GAN-generated synthetic data validation.

Contribution

The paper presents a novel five-stage trajectory clustering framework that effectively handles complex, real-world vehicle data and incorporates GAN-based synthetic data validation.

Findings

Framework achieves promising clustering results on real-world data.

Synthetic data generated by GANs can be validated for consistency with true clusters.

Abstract

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.