Cooperative adaptive cruise control for connected autonomous vehicles by factoring communication-related constraints
Cooperative adaptive cruise control for connected autonomous vehicles by factoring communication-related constraints
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DOI：
10.1016/j.trc.2019.04.010
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发表时间：
2020-04-01
影响因子：
8.3
通讯作者：
Peeta, Srinivas
中科院分区：
文献类型：
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作者：
Wang, Chaojie;Gong, Siyuan;Peeta, Srinivas
在联网自动驾驶车辆 (CAV) 背景下为编队提出的紧急协作自适应巡航控制 (CACC) 策略大多假设编队的理想化固定信息流拓扑 (IFT)，这意味着假设的 IFT 具有有保证的车辆对车辆 (V2V) 通信。实际上，由于干扰和信息拥塞等通信相关限制而导致故障，V2V 通信并不可靠。由于 CACC 策略需要连续的信息广播，因此在拥挤的 CAV 交通网络中可能会发生通信故障，导致排 IFT 动态变化。为了明确考虑 IFT 动态并利用它来增强 CACC 策略的性能，本研究提出了动态优化 CACC 的 IFT 的想法，称为 CACC-OIFT 策略。在 CACC-OIFT 下，队列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，从而在环境交通条件下优化队列性能。 CACC-OIFT 由 IFT 优化模型和自适应比例微分 (PD) 控制器组成。给定具有两个前驱跟随方案的自适应 PD 控制器，以及时间段开始之前的环境交通状况和队列大小，IFT 优化模型确定了最佳 IFT，该 IFT 可以在速度振荡能量方面最大化预期串稳定性。这种期望是因为每个 IFT 都有特定的退化场景，其概率由基于环境流量条件的该时间段内的通信失败概率确定。在该时间段内部署最佳 IFT，自适应 PD 控制器根据该时间段内每个时刻展开的退化场景不断确定车辆的跟车行为。所提出的 CACC-OIFT 的有效性通过基于 NGSIM 现场数据的 NS-3 数值实验得到验证。结果表明，所提出的 CACC-OIFT 可以在不可靠的 V2V 通信环境中显着增强排控制的串稳定性，优于具有固定 IFT 或 IFT 动态被动自适应方案的 CACC。
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 platoons 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.