Rethinking Closed-loop Planning Framework for Imitation-based Model Integrating Prediction and Planning

TL;DR

This paper introduces a novel closed-loop planning framework that integrates neural network-based prediction and planning, enhancing safety and stability in autonomous driving scenarios.

Contribution

It proposes a new framework with dual modes for joint prediction and planning, improving safety and stability without changing neural network architecture.

Findings

Ensures feasibility and local stability in planning.

Maintains safety through CMP safety monitoring.

Achieves substantial improvement over existing learning-based methods.

Abstract

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.