Autonomous transportation systems such as road vehicles or vessels require
the consideration of the static and dynamic environment to dislocate without
collision. Anticipating the behavior of an agent in a given situation is
required to adequately react to it in time. Developing deep learning-based
models has become the dominant approach to motion prediction recently. The
social environment is often considered through a CNN-LSTM-based sub-module
processing a $\textit{social tensor}$ that includes information of the past
trajectory of surrounding agents. For the proposed transformer-based trajectory
prediction model, an alternative, computationally more efficient social tensor
definition and processing is suggested. It considers the interdependencies
between target and surrounding agents at each time step directly instead of
relying on information of last hidden LSTM states of individually processed
agents. A transformer-based sub-module, the Social Tensor Transformer, is
integrated into the overall prediction model. It is responsible for enriching
the target agent's dislocation features with social interaction information
obtained from the social tensor. For the awareness of spatial limitations,
dislocation features are defined in relation to the navigable area. This
replaces additional, computationally expensive map processing sub-modules. An
ablation study shows, that for longer prediction horizons, the deviation of the
predicted trajectory from the ground truth is lower compared to a spatially and
socially agnostic model. Even if the performance gain from a spatial-only to a
spatial and social context-sensitive model is small in terms of common error
measures, by visualizing the results it can be shown that the proposed model in
fact is able to predict reactions to surrounding agents and explicitely allows
an interpretable behavior.

提出了一种基于转换器的轨迹预测模型，通过社交张量将目标代理的位移特征丰富化，考虑和周围代理的社交互动信息，以实现对周围代理的反应预测。

自主系统的空间和社会情境感知基于 Transformer 的轨迹预测

Spatial and social situation-aware transformer-based trajectory  prediction of autonomous systems

Accident detection and traffic analysis is a critical component of smart city
and autonomous transportation systems that can reduce accident frequency,
severity and improve overall traffic management. This paper presents a
comprehensive analysis of traffic accidents in different regions across the
United States using data from the National Highway Traffic Safety
Administration (NHTSA) Crash Report Sampling System (CRSS). To address the
challenges of accident detection and traffic analysis, this paper proposes a
framework that uses traffic surveillance cameras and action recognition systems
to detect and respond to traffic accidents spontaneously. Integrating the
proposed framework with emergency services will harness the power of traffic
cameras and machine learning algorithms to create an efficient solution for
responding to traffic accidents and reducing human errors. Advanced
intelligence technologies, such as the proposed accident detection systems in
smart cities, will improve traffic management and traffic accident severity.
Overall, this study provides valuable insights into traffic accidents in the US
and presents a practical solution to enhance the safety and efficiency of
transportation systems.

该研究综合分析了美国不同地区的交通事故，并提出了一个使用交通监控摄像头和动作识别系统进行事故检测和交通分析的框架，将该框架与紧急服务整合，以提高交通事故的响应效率并减少人为错误。此外，研究还探讨了智能交通事故检测系统在智能城市中的应用，旨在改善交通管理和减少交通事故的严重程度。

AI 在道路上：智慧城市中交通事故和事故检测系统的综合分析

AI on the Road: A Comprehensive Analysis of Traffic Accidents and  Accident Detection System in Smart Cities

Action detection and public traffic safety are crucial aspects of a safe
community and a better society. Monitoring traffic flows in a smart city using
different surveillance cameras can play a significant role in recognizing
accidents and alerting first responders. The utilization of action recognition
(AR) in computer vision tasks has contributed towards high-precision
applications in video surveillance, medical imaging, and digital signal
processing. This paper presents an intensive review focusing on action
recognition in accident detection and autonomous transportation systems for a
smart city. In this paper, we focused on AR systems that used diverse sources
of traffic video capturing, such as static surveillance cameras on traffic
intersections, highway monitoring cameras, drone cameras, and dash-cams.
Through this review, we identified the primary techniques, taxonomies, and
algorithms used in AR for autonomous transportation and accident detection. We
also examined data sets utilized in the AR tasks, identifying the main sources
of datasets and features of the datasets. This paper provides potential
research direction to develop and integrate accident detection systems for
autonomous cars and public traffic safety systems by alerting emergency
personnel and law enforcement in the event of road accidents to minimize human
error in accident reporting and provide a spontaneous response to victims

本篇论文通过对不同来源交通视频的分析与实验，提出了一种基于行为识别的自动化交通监控和事故检测系统，旨在提高公共交通安全和城市治理水平。