Self-driving cars face complex driving situations with a large amount of agents when moving in crowded cities. However, some of the agents are actually not influencing the behavior of the self-driving car. Filtering out unimportant agents would inherently simplify the behavior or motion planning task for the system. The planning system can then focus on fewer agents to find optimal behavior solutions for the ego~agent. This is helpful especially in terms of computational efficiency. In this paper, therefore, the research topic of importance filtering with driving risk models is introduced. We give an overview of state-of-the-art risk models and present newly adapted risk models for filtering. Their capability to filter out surrounding unimportant agents is compared in a large-scale experiment. As it turns out, the novel trajectory distance balances performance, robustness and efficiency well. Based on the results, we can further derive a novel filter architecture with multiple filter steps, for which risk models are recommended for each step, to further improve the robustness. We are confident that this will enable current behavior planning systems to better solve complex situations in everyday driving.

翻译：自动驾驶汽车在拥挤城市中行驶时，会面临涉及大量交通参与者的复杂驾驶场景。然而，部分交通参与者实际上并未对自动驾驶汽车的行为产生影响。过滤掉不重要的交通参与者本质上可简化系统的行为规划或运动规划任务。规划系统因此能聚焦于更少的交通参与者，从而为自车寻找最优行为解决方案。这在计算效率方面尤其具有优势。为此，本文引入基于驾驶风险模型的重要性过滤这一研究主题。我们概述了最先进的风险模型，并提出了用于过滤问题的新适应性风险模型。通过大规模实验，比较了各模型过滤无关交通参与者的能力。实验结果表明，新型轨迹距离指标在性能、鲁棒性和效率之间取得了良好平衡。基于实验结果，我们进一步推导出包含多级过滤步骤的新型过滤架构，并为每个步骤推荐了相应风险模型以提升鲁棒性。我们相信这将使当前的行为规划系统能更好地解决日常驾驶中的复杂场景。