# Leveraging Temporal Contexts to Enhance Vehicle-Infrastructure Cooperative Perception

arXiv: https://arxiv.org/abs/2408.10531 · HTML: https://mlsanity.com/paper/2408.10531/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2024.

## Built on by (most cited first, 8 of 8)

- [V2X-Radar: A Multi-modal Dataset with 4D Radar for Cooperative Perception](https://mlsanity.com/paper/2411.10962/) (2024)
- [Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark](https://mlsanity.com/paper/2503.06983/) (2025)
- [CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception](https://mlsanity.com/paper/2507.19239/) (2025)
- [Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition](https://mlsanity.com/paper/2507.21610/) (2025)
- [LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation](https://mlsanity.com/paper/2411.14927/) (2024)
- [SparseCoop: Cooperative Perception with Kinematic-Grounded Queries](https://mlsanity.com/paper/2512.06838/) (2025)
- [Long-SCOPE: Fully Sparse Long-Range Cooperative 3D Perception](https://mlsanity.com/paper/2604.09206/) (2026)
- [CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios](https://mlsanity.com/paper/2509.15984/) (2025)

## Abstract (arXiv)

Infrastructure sensors installed at elevated positions offer a broader perception range and encounter fewer occlusions. Integrating both infrastructure and ego-vehicle data through V2X communication, known as vehicle-infrastructure cooperation, has shown considerable advantages in enhancing perception capabilities and addressing corner cases encountered in single-vehicle autonomous driving. However, cooperative perception still faces numerous challenges, including limited communication bandwidth and practical communication interruptions. In this paper, we propose CTCE, a novel framework for cooperative 3D object detection. This framework transmits queries with temporal contexts enhancement, effectively balancing transmission efficiency and performance to accommodate real-world communication conditions. Additionally, we propose a temporal-guided fusion module to further improve performance. The roadside temporal enhancement and vehicle-side spatial-temporal fusion together constitute a multi-level temporal contexts integration mechanism, fully leveraging temporal information to enhance performance. Furthermore, a motion-aware reconstruction module is introduced to recover lost roadside queries due to communication interruptions. Experimental results on V2X-Seq and V2X-Sim datasets demonstrate that CTCE outperforms the baseline QUEST, achieving improvements of 3.8% and 1.3% in mAP, respectively. Experiments under communication interruption conditions validate CTCE's robustness to communication interruptions.