LHPF：自动驾驶中回顾历史与规划未来
LHPF: Look back the History and Plan for the Future in Autonomous Driving
AI总结：
针对现有自动驾驶模仿学习规划算法因独立评估当前与历史规划导致意图不连续、误差积累的问题，提出整合历史规划信息的LHPF规划器，通过历史意图聚合模块与空间查询向量解码轨迹，并引入舒适性辅助任务，实验证明其性能超现有方法且首超专家，模块在多骨干网络上应用潜力大。
摘要：
自动驾驶中的决策与规划对系统安全性至关重要，因此高效规划必不可少。当前基于模仿学习的规划算法常将历史轨迹与当前观测融合以预测未来候选路径，但这些算法通常独立评估当前和历史规划，导致驾驶意图不连续，且不连续规划每一步会积累误差。为应对这一挑战，本文提出LHPF——一种整合历史规划信息的模仿学习规划器。该方法采用历史意图聚合模块来汇聚历史规划意图，再将其与空间查询向量结合以解码最终规划轨迹。此外，我们引入舒适性辅助任务以提升驾驶行为的类人质量。利用真实世界和合成数据的大量实验表明，LHPF不仅在规划性能上超过现有先进的基于学习的规划器，还是首个纯基于学习的规划器超越专家的实例。此外，历史意图聚合模块在各种骨干网络上的应用凸显了所提方法的巨大潜力。代码将公开。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
