多车队列合并无需固定顺序，实现灵活高效协同行驶。
C2TE: Coordinated Constrained Task Execution Design for Ordering-Flexible Multi-Vehicle Platoon Merging
- 分两阶段设计：先拉距防撞，再灵活聚队
- 实现在无固定顺序下的安全队列合并
- 适合自动驾驶车队在复杂场景中快速适应
本文提出一种分布式协同约束任务执行（C2TE）算法，使来自不同车道的多辆车辆能够协作合并为一个有序灵活的车队，该车队不预设车辆的空间排列顺序。为此，将多车队列合并任务分为两个阶段：预合并调节与灵活队列合并，并分别建模为分布式约束优化问题。第一阶段通过控制屏障函数（CBF）约束实现纵向距离调节与同车道防撞，安全扩大相邻车辆间距；第二阶段通过编码横向收敛、纵向目标吸引及邻近防撞等子任务到CBF约束，高效实现灵活队列。灵活队列通过纵向目标吸引与时变邻近防撞约束的协同作用达成。在柔性排序引起的强非线性耦合下，提供了可行性保证与严格的收敛性分析。实验使用三辆自主移动车辆验证算法有效性与灵活性；大量仿真展示了其在车辆突发故障、新车辆加入、不同车道数、混合自主性及大规模场景下的鲁棒性、适应性与可扩展性。
原文摘要 · Abstract (English)
In this paper, we propose a distributed coordinated constrained task execution (C2TE) algorithm that enables a team of vehicles from different lanes to cooperatively merge into an {\it ordering-flexible platoon} maneuvering on the desired lane. Therein, the platoon is flexible in the sense that no specific spatial ordering sequences of vehicles are predetermined. To attain such a flexible platoon, we first separate the multi-vehicle platoon (MVP) merging mission into two stages, namely, pre-merging regulation and {\it ordering-flexible platoon} merging, and then formulate them into distributed constraint-based optimization problems. Particularly, by encoding longitudinal-distance regulation and same-lane collision avoidance subtasks into the corresponding control barrier function (CBF) constraints, the proposed algorithm in Stage 1 can safely enlarge sufficient longitudinal distances among adjacent vehicles. Then, by encoding lateral convergence, longitudinal-target attraction, and neighboring collision avoidance subtasks into CBF constraints, the proposed algorithm in Stage~2 can efficiently achieve the {\it ordering-flexible platoon}. Note that the {\it ordering-flexible platoon} is realized through the interaction of the longitudinal-target attraction and time-varying neighboring collision avoidance constraints simultaneously. Feasibility guarantee and rigorous convergence analysis are both provided under strong nonlinear couplings induced by flexible orderings. Finally, experiments using three autonomous mobile vehicles (AMVs) are conducted to verify the effectiveness and flexibility of the proposed algorithm, and extensive simulations are performed to demonstrate its robustness, adaptability, and scalability when tackling vehicles' sudden breakdown, new appearing, different number of lanes, mixed autonomy, and large-scale scenarios, respectively.
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