Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi 214122, China.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China.
Sensors (Basel). 2018 Oct 1;18(10):3307. doi: 10.3390/s18103307.
Multi-platooning is an important management strategy for autonomous driving technology. The backbone vehicles in a multi-platoon adopt the IEEE 802.11 distributed coordination function (DCF) mechanism to transmit vehicles' kinematics information through inter-platoon communications, and then forward the information to the member vehicles through intra-platoon communications. In this case, each vehicle in a multi-platoon can acquire the kinematics information of other vehicles. The parameters of DCF, the hidden terminal problem and the number of neighbors may incur a long and unbalanced one-hop delay of inter-platoon communications, which would further prolong end-to-end delay of inter-platoon communications. In this case, some vehicles within a multi-platoon cannot acquire the emergency changes of other vehicles' kinematics within a limited time duration and take prompt action accordingly to keep a multi-platoon formation. Unlike other related works, this paper proposes a swarming approach to optimize the one-hop delay of inter-platoon communications in a multi-platoon scenario. Specifically, the minimum contention window size of each backbone vehicle is adjusted to enable the one-hop delay of each backbone vehicle to get close to the minimum average one-hop delay. The simulation results indicate that, the one-hop delay of the proposed approach is reduced by 12% as compared to the DCF mechanism with the IEEE standard contention window size. Moreover, the end-to-end delay, one-hop throughput, end-to-end throughput and transmission probability have been significantly improved.
多车队编队是自动驾驶技术的一种重要管理策略。多车队中的骨干车辆采用 IEEE 802.11 分布式协调功能 (DCF) 机制,通过队列间通信传输车辆运动学信息,然后通过队列内通信将信息转发给成员车辆。在这种情况下,多车队中的每辆车都可以获取其他车辆的运动学信息。DCF 参数、隐藏终端问题和邻居数量可能会导致队列间通信的单跳延迟较长且不平衡,从而进一步延长队列间通信的端到端延迟。在这种情况下,多车队中的一些车辆在有限的时间内无法获取其他车辆运动学的紧急变化情况,无法及时采取行动来保持多车队编队。与其他相关工作不同,本文提出了一种群集方法来优化多车队场景中的队列间通信的单跳延迟。具体来说,调整每个骨干车辆的最小竞争窗口大小,以使每个骨干车辆的单跳延迟接近最小平均单跳延迟。仿真结果表明,与具有 IEEE 标准竞争窗口大小的 DCF 机制相比,所提出方法的单跳延迟降低了 12%。此外,端到端延迟、单跳吞吐量、端到端吞吐量和传输概率都得到了显著提高。