CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception

Cooperative perception aims to address the inherent limitations of single autonomous driving systems through information exchange among multiple agents. Previous research has primarily focused on single-frame perception tasks. However, the more challenging cooperative sequential perception tasks, such as cooperative 3D multi-object tracking, have not been thoroughly investigated.Therefore, we propose CoopTrack, a fully instance-level end-to-end framework for cooperative tracking, featuring learnable instance association, which fundamentally differs from existing approaches.CoopTrack transmits sparse instance-level features that significantly enhance perception capabilities while maintaining low transmission costs. Furthermore, the framework comprises three key components: Multi-Dimensional Feature Extraction (MDFE), Cross-Agent Alignment (CAA), and Graph-Based Association (GBA), which collectively enable comprehensive instance representation with semantic and motion features, and adaptive cross-agent association and fusion based on graph learning.Experiments on the V2X-Seq dataset demonstrate that, benefiting from its sophisticated design, CoopTrack achieves state-of-the-art performance, with 39.0\% mAP and 32.8\% AMOTA. Codes and visualization results are provided in the supplementary materials.