论文标题
将防御驾驶编码为动态纳什游戏
Encoding Defensive Driving as a Dynamic Nash Game
论文作者
论文摘要
部署在现实世界环境中的机器人应以强大的方式安全地运行。在“自我”代理在具有多种其他“非EGO”代理的环境中导航的情况下，通常提出了两种安全模式 - 对抗性鲁棒性和概率约束满意度。但是，尽管前者通常在计算上是棘手的，并且导致过度保守的解决方案，但后者通常依赖于强烈的分布假设，而忽略了代理之间的战略耦合。 为了避免这些弊端，我们在通用和动态游戏理论的框架内提出了一种新颖的鲁棒性表述，该理论以防御驾驶为基础。更确切地说，我们预先为自我代理的成本函数提供了对抗阶段。也就是说，我们预定了一个时间间隔，在此期间，假定其他代理会暂时分散注意力，以便在此期间对其他代理人对其他代理的潜在危险行为的均衡轨迹进行稳健。我们证明了通过多种流量方案来编码安全性的有效性。
Robots deployed in real-world environments should operate safely in a robust manner. In scenarios where an "ego" agent navigates in an environment with multiple other "non-ego" agents, two modes of safety are commonly proposed -- adversarial robustness and probabilistic constraint satisfaction. However, while the former is generally computationally intractable and leads to overconservative solutions, the latter typically relies on strong distributional assumptions and ignores strategic coupling between agents. To avoid these drawbacks, we present a novel formulation of robustness within the framework of general-sum dynamic game theory, modeled on defensive driving. More precisely, we prepend an adversarial phase to the ego agent's cost function. That is, we prepend a time interval during which other agents are assumed to be temporarily distracted, in order to render the ego agent's equilibrium trajectory robust against other agents' potentially dangerous behavior during this time. We demonstrate the effectiveness of our new formulation in encoding safety via multiple traffic scenarios.
