Decision-making and planning in autonomous driving critically reflect the safety of the system, making effective planning imperative. Current imitation learning-based planning algorithms often merge historical trajectories with present observations to predict future candidate paths. However, these algorithms typically assess the current and historical plans independently, leading to discontinuities in driving intentions and an accumulation of errors with each step in a discontinuous plan. To tackle this challenge, this paper introduces LHPF, an imitation learning planner that integrates historical planning information. Our approach employs a historical intention aggregation module that pools historical planning intentions, which are then combined with a spatial query vector to decode the final planning trajectory. Furthermore, we incorporate a comfort auxiliary task to enhance the human-like quality of the driving behavior. Extensive experiments using both real-world and synthetic data demonstrate that LHPF not only surpasses existing advanced learning-based planners in planning performance but also marks the first instance of a purely learning-based planner outperforming the expert. Additionally, the application of the historical intention aggregation module across various backbones highlights the considerable potential of the proposed method. The code will be made publicly available.

翻译：自动驾驶中的决策与规划至关重要地反映了系统的安全性，因此实现有效的规划势在必行。当前基于模仿学习的规划算法通常将历史轨迹与当前观测融合以预测未来的候选路径。然而，这些算法通常独立评估当前与历史的规划，导致驾驶意图的不连续性，并在不连续的规划中随着每一步产生误差累积。为应对这一挑战，本文提出了LHPF，一种整合历史规划信息的模仿学习规划器。我们的方法采用了一个历史意图聚合模块，该模块汇集历史规划意图，随后将其与空间查询向量结合以解码最终的规划轨迹。此外，我们引入了一项舒适度辅助任务，以提升驾驶行为类人化的质量。利用真实世界与合成数据进行的广泛实验表明，LHPF不仅在规划性能上超越了现有先进的基于学习的规划器，而且标志着纯基于学习的规划器首次超越专家表现。此外，历史意图聚合模块在不同骨干网络上的应用突显了所提方法的巨大潜力。代码将公开提供。