End-to-End 3-D Spatiotemporal Perception with Multimodal Fusion and V2X Collaboration

Multiview cooperative perception and multimodal fusion are essential for reliable 3-D spatiotemporal understanding in autonomous driving, especially in cases with occlusions, limited viewpoints, and communication delays in vehicle-to-everything (V2X) scenarios. In this paper, Cross-modal End-to-End Tracking for V2X (XET-V2X), a multimodal fused end-to-end tracking framework for V2X collaboration that unifies multiview multimodal sensing within a shared spatiotemporal representation, is proposed. To efficiently align heterogeneous viewpoints and modalities, XET-V2X introduces a dual-layer spatial cross-attention module based on multiscale deformable attention. Multiview image features are aggregated to enhance semantic consistency, followed by point cloud fusion guided by the updated spatial queries, enabling effective cross-modal interaction while reducing computational overhead. Experiments based on the real-world V2X Sequential Perception Dataset (V2X-Seq-SPD) dataset and two simulated V2X-Sim-derived subsets, namely the vehicle-to-vehicle (V2X-Sim-V2V) and vehicle-to-infrastructure (V2X-Sim-V2I) subsets, demonstrate consistent improvements in detection and tracking performance under varying communication delays, with XET-V2X achieving up to 15-20% relative gains in mean average precision (mAP) and average multi-object tracking accuracy (AMOTA) over single-view or single-modal baselines, while also outperforming representative tracking-by-detection cooperative perception methods.