Emergent cooperative adaptive cruise control (CACC) strategies being proposed for platoon formation in the connected autonomous vehicle (CAV) context mostly assume idealized fixed information flow topologies (IFTs) for the platoon, implying guaranteed vehicle-to-vehicle (V2V) communications for the IFT assumed. In reality, V2V communications are unreliable due to failures resulting from communication-related constraints such as interference and information congestion. Since CACC strategies entail continuous information broadcasting, communication failures can occur in congested CAV traffic networks, leading to a platoon’s IFT varying dynamically. To explicitly factor IFT dynamics and to leverage it to enhance the performance of CACC strategies, this study proposes the idea of dynamically optimizing the IFT for CACC, labeled the CACC-OIFT strategy. Under CACC-OIFT, the vehicles in the platoon cooperatively determine in real-time which vehicles will dynamically deactivate or activate the “send” functionality of their V2V communication devices to generate IFTs that optimize the platoon performance in terms of string stability under the ambient traffic conditions. The CACC-OIFT consists of an IFT optimization model and an adaptive Proportional-Derivative (PD) controller. Given the adaptive PD controller with a two-predecessor-following scheme, and the ambient traffic conditions and the platoon size just before the start of a time period, the IFT optimization model determines the optimal IFT that maximizes the expected string stability in terms of the energy of speed oscillations. This expectation is because each IFT has specific degeneration scenarios whose probabilities are determined by the communication failure probabilities for that time period based on the ambient traffic conditions. The optimal IFT is deployed for that time period, and the adaptive PD controller continuously determines the car-following behaviors of the vehicles based on the unfolding degeneration scenario for each time instant within that period. The effectiveness of the proposed CACC-OIFT is validated through numerical experiments in NS-3 based on NGSIM field data. The results indicate that the proposed CACC-OIFT can significantly enhance the string stability of platoon control in an unreliable V2V communication context, outperforming CACCs with fixed IFTs or with passive adaptive schemes for IFT dynamics.

针对联网无人驾驶汽车（CAV）的队列形成提出的紧急协作式自适应巡航控制（CACC）策略大多假设该队列的理想化固定信息流拓扑（IFT），这意味着该车的车对车（V2V）通信得到保证。假定为IFT。实际上，由于与通信有关的约束（例如干扰和信息拥塞）导致的故障，V2V通信是不可靠的。由于CACC策略需要连续的信息广播，因此在拥挤的CAV交通网络中可能发生通信故障，从而导致排的IFT动态变化。为了明确考虑IFT动态因素并利用它来提高CACC策略的性能，本研究提出了一种动态优化CACC IFT的想法，称为CACC-OIFT策略。在CACC-OIFT下，排中的车辆实时协作确定哪些车辆将动态停用或激活其V2V通信设备的“发送”功能，以生成IFT，从而根据周围交通状况下的弦稳定性来优化排性能。条件。CACC-OIFT由IFT优化模型和自适应比例微分（PD）控制器组成。给定具有两个前驱方案的自适应PD控制器，并在某个时间段开始之前确定周围的交通状况和排的大小，则IFT优化模型将确定最佳IFT，该IFT将根据速度振荡的能量。这种期望是因为每个IFT都有特定的退化方案，其概率由基于环境交通状况的该时间段内的通信故障概率来确定。在该时间段内部署了最佳IFT，并且自适应PD控制器会根据该时间段内每个时刻的展开退化场景，连续确定车辆的跟车行为。通过基于NGSIM现场数据的NS-3数值试验，验证了所提出的CACC-OIFT的有效性。结果表明，提出的CACC-OIFT可以在不可靠的V2V通信环境中显着增强排控制的字符串稳定性，在固定IFT或无源自适应IFT动态方案方面优于CACC。