Motion planning is an essential element of the modular architecture of autonomous vehicles, serving as a bridge between upstream perception modules and downstream low-level control signals. Traditional motion planners were initially designed for specific Automated Driving Functions (ADFs), yet the evolving landscape of highly automated driving systems (ADS) requires motion for a wide range of ADFs, including unforeseen ones. This need has motivated the development of the ``hybrid" approach in the literature, seeking to enhance motion planning performance by combining diverse techniques, such as data-driven (learning-based) and logic-driven (analytic) methodologies. Recent research endeavours have significantly contributed to the development of more efficient, accurate, and safe hybrid methods for Tactical Decision Making (TDM) and Trajectory Generation (TG), as well as integrating these algorithms into the motion planning module. Owing to the extensive variety and potential of hybrid methods, a timely and comprehensive review of the current literature is undertaken in this survey article. We classify the hybrid motion planners based on the types of components they incorporate, such as combinations of sampling-based with optimization-based/learning-based motion planners. The comparison of different classes is conducted by evaluating the addressed challenges and limitations, as well as assessing whether they focus on TG and/or TDM. We hope this approach will enable the researchers in this field to gain in-depth insights into the identification of current trends in hybrid motion planning and shed light on promising areas for future research.

翻译：运动规划是自动驾驶汽车模块化架构的核心要素，在感知模块与底层控制信号之间起着桥梁作用。传统运动规划器最初为特定自动驾驶功能（ADFs）设计，然而高度自动化驾驶系统（ADS）的快速发展要求运动规划能够适应包括未知场景在内的广泛ADFs。这一需求推动了文献中"混合"方法的发展，旨在通过融合数据驱动（基于学习）与逻辑驱动（基于解析）等多元技术来提升运动规划性能。近期研究在战术决策（TDM）与轨迹生成（TG）的高效、精确、安全混合方法开发方面取得显著进展，同时推动了这些算法在运动规划模块中的集成。鉴于混合方法的多样性与潜力，本文对现有文献进行了及时全面的梳理。我们根据混合运动规划器所整合的组件类型进行分类，例如基于采样的规划器与基于优化/基于学习的规划器相结合等方案。通过评估各类方法应对的挑战与局限，并分析其侧重于TG和/或TDM的程度，我们对不同类别进行了系统性比较。期望本综述能帮助领域研究者深入理解混合运动规划的当前发展趋势，并为未来研究方向提供启示。