Adaptive Safe Merging Control for Heterogeneous Autonomous Vehicles using Parametric Control Barrier Functions

Adaptive Safe Merging Control for Heterogeneous Autonomous Vehicles using Parametric Control Barrier Functions
复制标题

使用参数控制屏障函数的异构自主车辆自适应安全并道控制

DOI:
10.1109/iv51971.2022.9827329
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发表时间:
2022
期刊:
2022 IEEE Intelligent Vehicles Symposium (IV)
影响因子:
--
通讯作者:
J. Dolan
J. Dolan
中科院分区:
--
文献类型:
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作者:
Yiwei Lyu;Wenhao Luo;J. Dolan

文献摘要

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随着对机器人安全自主性的日益重视,基于模型的安全控制方法如控制障碍函数已被广泛研究,以确保在机器人之间的相互作用的安全性。在本文中,我们引入了参数控制障碍函数(参数CBF),传统的控制障碍函数的一种新的变体,以扩展其表达能力,在描述不同的安全行为之间的异构机器人。而不是假设合作和同质的机器人使用相同的安全控制器,自我机器人能够通过不同的参数CBF与观察到的数据模型的相邻机器人的底层安全控制器。给定学习的参数CBF和证明的前向不变性,它为自我机器人提供了更大的灵活性,以更好地与其他异构机器人协调,提高效率,同时享受形式上可证明的安全保证。我们展示了使用参数CBF的行为预测和自适应安全控制的坡道合并的情况下,从自动驾驶的应用。与传统的CBF相比,参数CBF的优点是捕捉不同的驱动程序的特性,提供更丰富的描述机器人的行为在安全控制的上下文中。数值仿真验证了该方法的有效性。
With the increasing emphasis on the safe autonomy for robots, model-based safe control approaches such as Control Barrier Functions have been extensively studied to ensure guaranteed safety during inter-robot interactions. In this paper, we introduce the Parametric Control Barrier Function (Parametric-CBF), a novel variant of the traditional Control Barrier Function to extend its expressivity in describing different safe behaviors among heterogeneous robots. Instead of assuming cooperative and homogeneous robots using the same safe controllers, the ego robot is able to model the neighboring robots’ underlying safe controllers through different Parametric-CBFs with observed data. Given learned parametric-CBF and proved forward invariance, it provides greater flexibility for the ego robot to better coordinate with other heterogeneous robots with improved efficiency while enjoying formally provable safety guarantees. We demonstrate the usage of Parametric-CBF in behavior prediction and adaptive safe control in the ramp merging scenario from the applications of autonomous driving. Compared to traditional CBF, Parametric-CBF has the advantage of capturing varying drivers’ characteristics given richer description of robot behavior in the context of safe control. Numerical simulations are given to validate the effectiveness of the proposed method.