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.

自主车辆与行人共享空间时，规划行驶路径需要考虑行人的未来轨迹。本文系统地回顾了文献中用于在存在车辆的情况下建模行人轨迹预测的不同方法，研究了与行人 - 车辆交互作用相关的特定考虑因素，并回顾了先前提出的预测模型中如何考虑预测不确定性和行为差异等不同变量。同时，文章也提供了包含行人和车辆轨迹数据的数据集概述，讨论了未来工作的研究空白和方向，如在深度学习方法中更有效地定义交互主体以及在无结构环境中收集更多混合交通数据集的需求。

行人 - 车辆混合环境中的行人轨迹预测：一项系统综述

Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed  Environments: A Systematic Review

Autonomous vehicles (AVs) are becoming an indispensable part of future
transportation. However, safety challenges and lack of reliability limit their
real-world deployment. Towards boosting the appearance of AVs on the roads, the
interaction of AVs with pedestrians including "prediction of the pedestrian
crossing intention" deserves extensive research. This is a highly challenging
task as involves multiple non-linear parameters. In this direction, we extract
and analyse spatio-temporal visual features of both pedestrian and traffic
contexts. The pedestrian features include body pose and local context features
that represent the pedestrian's behaviour. Additionally, to understand the
global context, we utilise location, motion, and environmental information
using scene parsing technology that represents the pedestrian's surroundings,
and may affect the pedestrian's intention. Finally, these multi-modality
features are intelligently fused for effective intention prediction learning.
The experimental results of the proposed model on the JAAD dataset show a
superior result on the combined AUC and F1-score compared to the
state-of-the-art.

该研究使用计算机视觉和多模态特征识别，以实现自主车辆与行人之间的安全互动，并在 JAAD 数据集上展示了比现有技术更优秀的预测结果。

行人意图预测的局部和全局情境特征融合

Local and Global Contextual Features Fusion for Pedestrian Intention  Prediction

In this paper, we study the interaction between pedestrians and vehicles and
propose a novel neural network structure called the Pedestrian-Vehicle
Interaction (PVI) extractor for learning the pedestrian-vehicle interaction. We
implement the proposed PVI extractor on both sequential approaches (long
short-term memory (LSTM) models) and non-sequential approaches (convolutional
models). We use the Waymo Open Dataset that contains real-world urban traffic
scenes with both pedestrian and vehicle annotations. For the LSTM-based models,
our proposed model is compared with Social-LSTM and Social-GAN, and using our
proposed PVI extractor reduces the average displacement error (ADE) and the
final displacement error (FDE) by 7.46% and 5.24%, respectively. For the
convolutional-based models, our proposed model is compared with Social-STGCNN
and Social-IWSTCNN, and using our proposed PVI extractor reduces the ADE and
FDE by 2.10% and 1.27%, respectively. The results show that the
pedestrian-vehicle interaction influences pedestrian behavior, and the models
using the proposed PVI extractor can capture the interaction between
pedestrians and vehicles, and thereby outperform the compared methods.

本文研究了行人和车辆之间的交互作用，提出了一个新的神经网络模型，称为行人 - 车辆交互提取器（PVI），用于学习行人和车辆之间的交互。通过实现该模型在两种不同的方法上，作者可以减少行人的平均位移误差（ADE）和最终位移误差（FDE），该模型可以捕捉行人和车辆之间的交互并优于现有的方法。