Autonomous vehicles face tremendous challenges while interacting with human drivers in different kinds of scenarios. Developing control methods with safety guarantees while performing interactions with uncertainty is an ongoing research goal. In this paper, we present a real-time safe control framework using bi-level optimization with Control Barrier Function (CBF) that enables an autonomous ego vehicle to interact with human-driven cars in ramp merging scenarios with a consistent safety guarantee. In order to explicitly address motion uncertainty, we propose a novel extension of control barrier functions to a probabilistic setting with provable chance-constrained safety and analyze the feasibility of our control design. The formulated bi-level optimization framework entails first choosing the ego vehicle's optimal driving style in terms of safety and primary objective, and then minimally modifying a nominal controller in the context of quadratic programming subject to the probabilistic safety constraints. This allows for adaptation to different driving strategies with a formally provable feasibility guarantee for the ego vehicle's safe controller. Experimental results are provided to demonstrate the effectiveness of our proposed approach.

中文翻译：

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概率安全的自动驾驶汽车自适应合并控制

自动驾驶汽车在各种场景下与人类驾驶员互动时面临着巨大的挑战。在进行具有不确定性的相互作用的同时开发具有安全保证的控制方法是一项持续不断的研究目标。在本文中，我们提出了一种使用带有控制障碍功能（CBF）的双层优化的实时安全控制框架，该框架使自动自主车辆能够在坡道合并场景中与人为驱动的车辆进行交互，并具有一致的安全保证。为了明确解决运动不确定性，我们提出了将控制屏障功能扩展到具有可证明的机会约束安全性的概率设置的新方法，并分析了我们的控制设计的可行性。制定的双层优化框架需要首先选择“自我”工具 安全性和主要目标方面的最佳驾驶风格，然后在二次编程的情况下根据概率安全性约束最小地修改标称控制器。这样就可以针对自我驾驶安全控制器的形式上可证明的可行性保证来适应不同的驾驶策略。提供实验结果以证明我们提出的方法的有效性。