Considerable research efforts have been devoted to the development of motion planning algorithms, which form a cornerstone of the autonomous driving system (ADS). However, obtaining an interactive and secure trajectory for the ADS remains a formidable task, especially in scenarios with significant interaction complexities. Many contemporary prediction-based planning methods frequently overlook interaction modeling, leading to less effective planning performance. This paper introduces a novel prediction-based interactive planning framework that explicitly and mathematically models interactions among traffic entities during the planning process. Our method incorporates interaction reasoning into spatio-temporal (s-t) planning by defining interaction conditions and constraints. Furthermore, it records and continually updates interaction relations for each planned state throughout the forward search. We assess the performance of our approach alongside state-of-the-art methods using a series of experiments conducted in both single and multi-modal scenarios. These experiments encompass variations in the accuracy of prediction outcomes and different degrees of planner aggressiveness. The experimental findings demonstrate the effectiveness and robustness of our method, yielding insights applicable to the wider field of autonomous driving. For the community's reference, our code is accessible at https://github.com/ChenYingbing/IR-STP-Planner.

翻译：大量研究工作致力于开发运动规划算法，这是自动驾驶系统（ADS）的核心组成部分。然而，为ADS获取交互式且安全的轨迹仍是一项艰巨任务，尤其是在交互复杂度较高的场景中。当前许多基于预测的规划方法常常忽视交互建模，导致规划性能欠佳。本文提出了一种新颖的基于预测的交互式规划框架，该框架在规划过程中显式且数学化地建模交通实体间的交互。我们的方法通过定义交互条件与约束，将交互推理融入时空（s-t）规划中；此外，在前向搜索过程中记录并持续更新每个规划状态的交互关系。我们通过一系列在单模态与多模态场景下开展的实验（涵盖预测结果精度的变化及规划器激进程度的不同），与最先进方法共同评估了本方法的性能。实验结果表明了本方法的有效性和鲁棒性，并得出了可应用于广义自动驾驶领域的洞见。为便于社区参考，我们的代码已公开于https://github.com/ChenYingbing/IR-STP-Planner。