Automated driving has the potential to revolutionize personal, public, and freight mobility. Besides the enormous challenge of perception, i.e. accurately perceiving the environment using available sensor data, automated driving comprises planning a safe, comfortable, and efficient motion trajectory. To promote safety and progress, many works rely on modules that predict the future motion of surrounding traffic. Modular automated driving systems commonly handle prediction and planning as sequential separate tasks. While this accounts for the influence of surrounding traffic on the ego-vehicle, it fails to anticipate the reactions of traffic participants to the ego-vehicle's behavior. Recent works suggest that integrating prediction and planning in an interdependent joint step is necessary to achieve safe, efficient, and comfortable driving. While various models implement such integrated systems, a comprehensive overview and theoretical understanding of different principles are lacking. We systematically review state-of-the-art deep learning-based prediction, planning, and integrated prediction and planning models. Different facets of the integration ranging from model architecture and model design to behavioral aspects are considered and related to each other. Moreover, we discuss the implications, strengths, and limitations of different integration methods. By pointing out research gaps, describing relevant future challenges, and highlighting trends in the research field, we identify promising directions for future research.

翻译：自动驾驶有望革新个人出行、公共交通及货物运输。除了感知的巨大挑战（即利用可用传感器数据精确感知环境）外，自动驾驶还需规划安全、舒适且高效的行驶轨迹。为提升安全性与进展，许多研究依赖预测周围交通参与者未来运动的模块。模块化自动驾驶系统通常将预测和规划作为顺序独立的子任务处理。虽然这考虑了周围交通对自车的影响，但未能预见交通参与者对自车行为的反应。近期研究表明，在相互依存的联合步骤中整合预测与规划是实现安全、高效、舒适驾驶的必要条件。尽管各类模型已实现此类集成系统，但不同原理的全面概览与理论理解仍然缺失。本文系统综述了最先进的基于深度学习的预测、规划及集成预测与规划模型。从模型架构、模型设计到行为特征等不同整合维度均被纳入考量并建立关联。此外，我们讨论了不同集成方法的启示、优势与局限性。通过指出研究空白、描述未来关键挑战并强调研究领域趋势，我们识别出具有前景的未来研究方向。