Considerable research efforts have been devoted to the development of motion planning algorithms, which form a cornerstone of the autonomous driving system (ADS). Nonetheless, acquiring an interactive and secure trajectory for the ADS remains challenging due to the complex nature of interaction modeling in planning. Modern planning methods still employ a uniform treatment of prediction outcomes and solely rely on collision-avoidance strategies, leading to suboptimal planning performance. To address this limitation, this paper presents a novel prediction-based interactive planning framework for autonomous driving. Our method incorporates interaction reasoning into spatio-temporal (s-t) planning by defining interaction conditions and constraints. Specifically, 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 in the CommonRoad environment. Our experiments include a total of 232 scenarios, with variations in the accuracy of prediction outcomes, modality, and degrees of planner aggressiveness. The experimental findings demonstrate the effectiveness and robustness of our method. It leads to a reduction of collision times by approximately 17.6% in 3-modal scenarios, along with improvements of nearly 7.6% in distance completeness and 31.7% in the fail rate in single-modal scenarios. 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运动规划算法的开发，其构成了自动驾驶系统的基石。然而，由于规划中交互建模的复杂特性，为自动驾驶系统获取安全且具有交互性的轨迹仍具挑战性。现代规划方法仍对预测结果采用统一处理方式，并仅依赖避碰策略，导致规划性能欠佳。为解决此局限，本文提出一种新颖的基于预测的自动驾驶交互规划框架。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规划。具体而言，在前向搜索过程中，该方法记录并持续更新每个规划状态的交互关系。我们在CommonRoad环境中将本方法的性能与最先进方法进行了评估。实验共涵盖232个场景，涉及预测结果准确性、模态数及规划器激进程度的变化。实验结果表明了本方法的有效性与鲁棒性。在三模态场景中，碰撞次数减少约17.6%，同时在单模态场景中，距离完整性提升近7.6%，失败率下降31.7%。为供社区参考，我们的代码可通过https://github.com/ChenYingbing/IR-STP-Planner获取。