Traditionally, prediction and planning in autonomous driving (AD) have been treated as separate, sequential modules. Recently, there has been a growing shift towards tighter integration of these components, known as Integrated Prediction and Planning (IPP), with the aim of enabling more informed and adaptive decision-making. However, it remains unclear to what extent this integration actually improves planning performance. In this work, we investigate the role of prediction in IPP approaches, drawing on the widely adopted Val14 benchmark, which encompasses more common driving scenarios with relatively low interaction complexity, and the interPlan benchmark, which includes highly interactive and out-of-distribution driving situations. Our analysis reveals that even access to perfect future predictions does not lead to better planning outcomes, indicating that current IPP methods often fail to fully exploit future behavior information. Instead, we focus on high-quality proposal generation, while using predictions primarily for collision checks. We find that many imitation learning-based planners struggle to generate realistic and plausible proposals, performing worse than PDM - a simple lane-following approach. Motivated by this observation, we build on PDM with an enhanced proposal generation method, shifting the emphasis towards producing diverse but realistic and high-quality proposals. This proposal-centric approach significantly outperforms existing methods, especially in out-of-distribution and highly interactive settings, where it sets new state-of-the-art results.

翻译：传统上，自动驾驶中的预测与规划被视为独立且顺序执行的模块。近年来，学界日益倾向于将这两个组件更紧密地集成，即集成预测与规划（IPP），旨在实现更明智且自适应的决策。然而，这种集成究竟能在多大程度上提升规划性能仍不明确。本研究基于广泛采用的Val14基准（涵盖交互复杂度相对较低的常见驾驶场景）和interPlan基准（包含高度交互且分布外的驾驶情境），深入探讨了预测在IPP方法中的作用。我们的分析表明，即使获得完美的未来预测，也未能带来更优的规划结果，这揭示出现有IPP方法往往未能充分利用未来行为信息。因此，我们将研究重心转向高质量提案生成，而仅将预测主要用于碰撞检测。研究发现，许多基于模仿学习的规划器难以生成真实且合理的提案，其表现甚至不及简单的车道跟随方法PDM。基于这一观察，我们在PDM基础上构建了增强型提案生成方法，将重点转向生成多样但真实且高质量的提案。这种以提案为核心的方法显著超越了现有技术，尤其在分布外和高度交互的场景中取得了新的最先进成果。