Congestion-mitigating MPC design for adaptive cruise control based on Newell’s car following model: History outperforms prediction
Congestion-mitigating MPC design for adaptive cruise control based on Newell’s car following model: History outperforms prediction
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DOI：
10.1016/j.trc.2022.103801
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Hao Zhou;Anye Zhou;Tienan Li;Danjue Chen;S. Peeta;Jorge A. Laval
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文献类型：
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作者：
Hao Zhou;Anye Zhou;Tienan Li;Danjue Chen;S. Peeta;Jorge A. Laval
目前，用于自适应巡航控制(ACC)系统的模型预测控制(MPC)依赖于对领航者运动的预测来规划跟随者的轨迹。然而，这样的预测必须准确，以保证字符串的稳定性，这对机器学习方法来说是一个持续的挑战。这个问题可以通过简单地结合领导者的历史来规避，该历史源于Newell的跟车(CF)模型，该模型中拥堵状态下的轨迹对应于领导者过去轨迹的时空变化。利用这一认识，本文在Newell的CF模型的基础上发展了一类MPC模型，命名为Newell MPC，该模型是安全的，可以减少交通拥堵。具体地说，我们首先提出了基线Newell MPC来复制原始Newell的CF模型，包括X b o u n d-Model和X r e f-Model，X b o u n d-Model以移动的先导轨迹为上包络，X r e f-Model以移动的先导轨迹为参考，避免了硬约束引发的不可行解问题。为了进一步提高控制性能，我们提出了X-V模型，它利用先导速度历史作为额外的参考，以增强模型的鲁棒性，并调节过/欠射击速度。此外，我们通过合并多个领导者对单领导者的Newell模型进行了扩展，提出了X-mu L模型，该模型可以实现驾驶员的预期，并相应地减少反应时间，提高管柱稳定性。最后，在X V模型的基础上，我们提出了两个额外的扩展：(I)X V r e L a x模型，它引入了驾驶员松弛来实现对合并交通的平稳响应；以及(Ii)X V S S模型，它实现了严格的弦稳定，以进一步抑制交通振荡。建议的Newell MPC使用OpenPilot和Comma在2019款本田思域的库存上进行了数值模拟和现场研究。AI；源代码可以在https://github.上找到Com/HaoZhouGT/OpenPilot。
Currently, model predictive control (MPC) for adaptive cruise control (ACC) systems relies on the prediction of the leader’s motion to plan the follower’s trajectory. However, such predictions must be accurate to guarantee string stability, which represents an ongoing challenge for machine learning approaches. This issue can be circumvented by simply incorporating the leader’s history, which follows from Newell’s car-following (CF) model where a trajectory under congestion corresponds to a temporal–spatial shift of the leader’s past trajectories. By leveraging this insight, this paper develops a family of MPC models based on Newell’s CF model, labeled Newell MPCs, which are safe and can reduce traffic congestion. Specifically, We first present baseline Newell MPCs to replicate the original Newell’s CF model, including the X b o u n d-Model, which uses the shifted leader trajectory as an upper envelope; and the X r e f-Model, which adopts the shifted leader trajectory as a reference to avoid the issue of infeasible solution triggered by hard constraints. To further improve the control performance, we propose the X V-Model which uses the leader speed history as an additional reference to enhance the model robustness and regulate speed over/under-shootings. In addition, we extend the single-leader Newell’s model through incorporating multiple leaders and propose the X m u l-Model, which can achieve driver anticipation, and correspondingly reduce reaction time and improve string stability. Finally, based on the X V-Model, we present two additional extensions:(i) the X V r e l a x-Model, which incorporates driver relaxation to achieve smooth response to merging traffic; and (ii) the X V s s-Model, which achieves strict string stability to further dampen traffic oscillations. The proposed Newell MPCs are tested using both numerical simulations and field studies on a stock 2019 Honda Civic using Openpilot and Comma. ai; the source code is available at https://github. com/HaoZhouGT/openpilot.