  In recent years, the integration of prediction and planning through neural
networks has received substantial attention. Despite extensive studies on it,
there is a noticeable gap in understanding the operation of such models within
a closed-loop planning setting. To bridge this gap, we propose a novel
closed-loop planning framework compatible with neural networks engaged in joint
prediction and planning. The framework contains two running modes, namely
planning and safety monitoring, wherein the neural network performs Motion
Prediction and Planning (MPP) and Conditional Motion Prediction (CMP)
correspondingly without altering architecture. We evaluate the efficacy of our
framework using the nuPlan dataset and its simulator, conducting closed-loop
experiments across diverse scenarios. The results demonstrate that the proposed
framework ensures the feasibility and local stability of the planning process
while maintaining safety with CMP safety monitoring. Compared to other
learning-based methods, our approach achieves substantial improvement.
