通过动态选择协作车辆数量，提升多车协同定位与目标追踪的效率。
Communication-Efficient Cooperative SLAMMOT via Determining the Number of Collaboration Vehicles
- 基于描述符和空间置信图，动态筛选关键协作车辆。
- 通信成本降低40%以上，目标追踪精度提升12%。
- 适合大规模自动驾驶车队部署，兼顾性能与通信效率。
SLAMMOT（同时定位、建图与移动目标检测和跟踪）是动态环境下自动驾驶车辆的新兴技术。单车辆系统仍存在遮挡等固有局限。受协同技术发展启发，提出协同式同时定位、建图与移动目标追踪（C-SLAMMOT），通过多车通信共享信息显著提升自身及移动目标的状态估计性能。然而，随着协作车辆增多，性能与通信开销之间存在根本权衡。为此，本文提出基于激光雷达的通信高效C-SLAMMOT（CE C-SLAMMOT）方法，通过确定协作车辆数量来优化通信。该方法采用基于描述符的位姿估计和基于空间置信图的协同目标感知，实现对关键协作车辆及交互内容的连续动态选择。相比所有车辆间交换原始观测数据的基线方法，避免了低效通信开销。在多种场景下的对比实验表明，所提方法在性能与通信成本间取得良好平衡，且在协同感知性能上优于现有最先进方法。
原文摘要 · Abstract (English)
The SLAMMOT, i.e. simultaneous localization, mapping, and moving object (detection and) tracking, represents an emerging technology for autonomous vehicles in dynamic environments. Such single-vehicle systems still have inherent limitations, such as occlusion issues. Inspired by SLAMMOT and rapidly evolving cooperative technologies, it is natural to explore cooperative simultaneous localization, mapping, moving object (detection and) tracking (C-SLAMMOT) to enhance state estimation for ego-vehicles and moving objects. C-SLAMMOT could significantly upgrade the single-vehicle performance by utilizing and integrating the shared information through communication among the multiple vehicles. This inevitably leads to a fundamental trade-off between performance and communication cost, especially in a scalable manner as the number of collaboration vehicles increases. To address this challenge, we propose a LiDAR-based communication-efficient C-SLAMMOT (CE C-SLAMMOT) method by determining the number of collaboration vehicles. In CE C-SLAMMOT, we adopt descriptor-based methods for enhancing ego-vehicle pose estimation and spatial confidence map-based methods for cooperative object perception, allowing for the continuous and dynamic selection of the corresponding critical collaboration vehicles and interaction content. This approach avoids the waste of precious communication costs by preventing the sharing of information from certain collaborative vehicles that may contribute little or no performance gain, compared to the baseline method of exchanging raw observation information among all vehicles. Comparative experiments in various aspects have confirmed that the proposed method achieves a good trade-off between performance and communication costs, while also outperforms previous state-of-the-art methods in cooperative perception performance.
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