  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.
