We address the problem of controlling Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network subject to hard safety constraints. It has been shown that such problems can be solved through a combination of tractable optimal control problems and Control Barrier Functions (CBFs) that guarantee the satisfaction of all constraints. These solutions can be reduced to a sequence of Quadratic Programs (QPs) which are efficiently solved on line over discrete time steps. However, guaranteeing the feasibility of the CBF-based QP method within each discretized time interval requires the careful selection of time steps which need to be sufficiently small. This creates computational requirements and communication rates between agents which may hinder the controller's application to real CAVs. In this paper, we overcome this limitation by adopting an event-triggered approach for CAVs in a conflict area such that the next QP is triggered by properly defined events with a safety guarantee. We present a laboratory-scale test bed we have developed to emulate merging roadways using mobile robots as CAVs which can be used to demonstrate how the event-triggered scheme is computationally efficient and can handle measurement uncertainties and noise compared to time-driven control while guaranteeing safety.

翻译：本文研究了在硬安全约束下，交通网络冲突区域中网联自动驾驶汽车（CAVs）的控制问题。已有研究表明，此类问题可通过结合可处理的最优控制问题与保证所有约束满足的控制屏障函数（CBFs）来解决。这些解可简化为一系列二次规划（QPs），并在离散时间步长上高效在线求解。然而，为确保基于CBF的QP方法在每个离散时间区间内的可行性，需谨慎选择足够小的时间步长。这带来了计算需求与智能体间的通信速率问题，可能阻碍控制器在实际CAVs中的应用。为克服这一限制，本文采用事件触发方法处理冲突区域中的CAVs，使下一个QP由适当定义的安全保证事件触发。我们介绍了一个实验室规模的试验平台，该平台利用移动机器人模拟CAVs并演示合并道路场景，结果表明与时间驱动控制相比，事件触发方案在保证安全性的同时，具有计算效率高、能处理测量不确定性与噪声的优势。