Abrupt maneuvers by surrounding vehicles (SVs) can typically lead to safety concerns and affect the task efficiency of the ego vehicle (EV), especially with model uncertainties stemming from environmental disturbances. This paper presents a real-time fail-operational controller that ensures the asymptotic convergence of an uncertain EV to a safe state, while preserving task efficiency in dynamic environments. An incremental Bayesian learning approach is developed to facilitate online learning and inference of changing environmental disturbances. Leveraging disturbance quantification and constraint transformation, we develop a stochastic fail-operational barrier based on the control barrier function (CBF). With this development, the uncertain EV is able to converge asymptotically from an unsafe state to a defined safe state with probabilistic stability. Subsequently, the stochastic fail-operational barrier is integrated into an efficient fail-operational controller based on quadratic programming (QP). This controller is tailored for the EV operating under control constraints in the presence of environmental disturbances, with both safety and efficiency objectives taken into consideration. We validate the proposed framework in connected cruise control (CCC) tasks, where SVs perform aggressive driving maneuvers. The simulation results demonstrate that our method empowers the EV to swiftly return to a safe state while upholding task efficiency in real time, even under time-varying environmental disturbances.

翻译：周围车辆(SVs)的突发操作通常会导致安全问题，并影响自车(EV)的任务效率，尤其在环境扰动引发的模型不确定性下。本文提出一种实时故障可运行控制器，确保不确定的EV在动态环境中渐进收敛至安全状态的同时保持任务效率。通过开发增量贝叶斯学习方法，实现对变化环境扰动的在线学习与推断。基于扰动量化和约束变换，我们构建了基于控制障碍函数(CBF)的随机故障可运行屏障。借助该设计，不确定的EV能够从非安全状态渐进收敛至具有概率稳定性的定义安全状态。进而将随机故障可运行屏障集成至基于二次规划(QP)的高效故障可运行控制器中。该控制器专为在环境扰动及控制约束下运行的EV定制，兼顾安全性与效率双重目标。我们在周围车辆执行激进驾驶操作的互联巡航控制(CCC)任务中验证了所提框架。仿真结果表明，即使在时变环境扰动下，该方法仍能使EV快速恢复安全状态且实时维持任务效率。