We consider the problem of group interactions in urban driving. State-of-the-art behavior planners for self-driving cars mostly consider each single agent-to-agent interaction separately in a cost function in order to find an optimal behavior for the ego agent, such as not colliding with any of the other agents. In this paper, we develop risk shadowing, a situation understanding method that allows us to go beyond single interactions by analyzing group interactions between three agents. Concretely, the presented method can find out which first other agent does not need to be considered in the behavior planner of an ego agent, because this first other agent cannot reach the ego agent due to a second other agent obstructing its way. In experiments, we show that using risk shadowing as an upstream filter module for a behavior planner allows to plan more decisive and comfortable driving strategies than state of the art, given that safety is ensured in these cases. The usability of the approach is demonstrated for different intersection scenarios and longitudinal driving.

翻译：我们考虑了城市驾驶中群体交互的问题。当前最先进的自动驾驶行为规划器大多在成本函数中单独处理每个智能体之间的交互，以寻找自车的最优行为（例如避免与任何其他智能体发生碰撞）。本文提出了风险遮蔽方法，这是一种情境理解方法，通过分析三个智能体之间的群体交互，使我们能够超越单一交互的局限。具体而言，该方法能够识别出哪些第一个其他智能体无需在自车的行为规划器中予以考虑，因为该第一个其他智能体受到第二个其他智能体的阻碍而无法到达自车。实验表明，在确保安全的前提下，将风险遮蔽作为行为规划器的上游过滤模块，能够规划出比现有方法更果断、更舒适的驾驶策略。该方法在不同交叉口场景及纵向驾驶场景中的可用性得到了验证。