行人-车辆混合环境中的行人轨迹预测：系统综述
Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review
AI总结：
本文遵循PRISMA流程系统综述车辆存在场景下的行人轨迹预测方法，筛选64篇论文，梳理模型、数据集、交互与不确定性处理，并指出未来研究方向。
摘要：
在与行人共享的空间中规划自动驾驶汽车（AV）的路径，需要对行人的未来轨迹进行推理。一种适用于AV的实用行人轨迹预测算法，必须考虑车辆与行人之间的交互对行人未来运动行为的影响。基于此，本文系统综述了文献中提出的、用于在车辆存在情况下建模行人轨迹预测并可应用于非结构化环境的不同方法。本文还考察了行人-车辆交互（相较于行人-行人交互）的特定考量，并综述了以往提出的预测模型如何处理预测不确定性和行为差异等不同变量。研究遵循PRISMA指南。未考虑车辆与行人交互或真实轨迹的论文，以及仅关注道路横穿的论文均被排除。检索共从ACM Digital Library、IEEE Xplore和Scopus数据库中识别出1260篇独特的同行评审论文。由于符合纳入和排除标准，最终有64篇论文被纳入综述。文中还概述了被综述论文所使用的、同时包含行人和车辆轨迹数据的数据集。最后讨论了研究空白与未来工作方向，例如在深度学习方法中更有效地定义交互智能体，以及需要收集更多非结构化环境中混合交通的数据集。
英文摘要：
Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction algorithm for the use of AVs needs to consider the effect of the vehicle's interactions with the pedestrians on pedestrians' future motion behaviours. In this regard, this paper systematically reviews different methods proposed in the literature for modelling pedestrian trajectory prediction in presence of vehicles that can be applied for unstructured environments. This paper also investigates specific considerations for pedestrian-vehicle interaction (compared with pedestrian-pedestrian interaction) and reviews how different variables such as prediction uncertainties and behavioural differences are accounted for in the previously proposed prediction models. PRISMA guidelines were followed. Articles that did not consider vehicle and pedestrian interactions or actual trajectories, and articles that only focused on road crossing were excluded. A total of 1260 unique peer-reviewed articles from ACM Digital Library, IEEE Xplore, and Scopus databases were identified in the search. 64 articles were included in the final review as they met the inclusion and exclusion criteria. An overview of datasets containing trajectory data of both pedestrians and vehicles used by the reviewed papers has been provided. Research gaps and directions for future work, such as having more effective definition of interacting agents in deep learning methods and the need for gathering more datasets of mixed traffic in unstructured environments are discussed.
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。
