Crash simulations play an essential role in improving vehicle safety, design
optimization, and injury risk estimation. Unfortunately, numerical solutions of
such problems using state-of-the-art high-fidelity models require significant
computational effort. Conventional data-driven surrogate modeling approaches
create low-dimensional embeddings for evolving the dynamics in order to
circumvent this computational effort. Most approaches directly operate on
high-resolution data obtained from numerical discretization, which is both
costly and complicated for mapping the flow of information over large spatial
distances. Furthermore, working with a fixed resolution prevents the adaptation
of surrogate models to environments with variable computing capacities,
different visualization resolutions, and different accuracy requirements. We
thus propose a multi-hierarchical framework for structurally creating a series
of surrogate models for a kart frame, which is a good proxy for
industrial-relevant crash simulations, at different levels of resolution. For
multiscale phenomena, macroscale features are captured on a coarse surrogate,
whereas microscale effects are resolved by finer ones. The learned behavior of
the individual surrogates is passed from coarse to finer levels through
transfer learning. In detail, we perform a mesh simplification on the kart
model to obtain multi-resolution representations of it. We then train a
graph-convolutional neural network-based surrogate that learns
parameter-dependent low-dimensional latent dynamics on the coarsest
representation. Subsequently, another, similarly structured surrogate is
trained on the residual of the first surrogate using a finer resolution. This
step can be repeated multiple times. By doing so, we construct multiple
surrogates for the same system with varying hardware requirements and
increasing accuracy.

提出了一种多层次框架，用于在不同分辨率级别上为碰撞模拟构建多个代理模型，以适应不同的硬件要求和增加的精度。

多层次代理学习结构动力学在使用图卷积神经网络的汽车碰撞安全中

Multi-Hierarchical Surrogate Learning for Structural Dynamics of  Automotive Crashworthiness Using Graph Convolutional Neural Networks

The field of intelligent connected in modern vehicles continues to expand,
and the functions of vehicles become more and more complex with the development
of the times. This has also led to an increasing number of vehicle
vulnerabilities and many safety issues. Therefore, it is particularly important
to identify high-risk vehicle intelligent connected systems, because it can
inform security personnel which systems are most vulnerable to attacks,
allowing them to conduct more thorough inspections and tests. In this paper, we
develop a new model for vehicle risk assessment by combining I-FAHP with FCA
clustering: VSRQ model. We extract important indicators related to vehicle
safety, use fuzzy cluster analys (FCA) combined with fuzzy analytic hierarchy
process (FAHP) to mine the vulnerable components of the vehicle intelligent
connected system, and conduct priority testing on vulnerable components to
reduce risks and ensure vehicle safety. We evaluate the model on OpenPilot and
experimentally demonstrate the effectiveness of the VSRQ model in identifying
the safety of vehicle intelligent connected systems. The experiment fully
complies with ISO 26262 and ISO/SAE 21434 standards, and our model has a higher
accuracy rate than other models. These results provide a promising new research
direction for predicting the security risks of vehicle intelligent connected
systems and provide typical application tasks for VSRQ. The experimental
results show that the accuracy rate is 94.36%, and the recall rate is 73.43%,
which is at least 14.63% higher than all other known indicators.

本文提出了一种基于 I-FAHP 和 FCA clustering 相结合的车辆风险评估模型（VSRQ model），并提取与车辆安全相关的重要指标，利用模糊聚类分析（FCA）和模糊层次分析（FAHP）来挖掘车辆智能连接系统的易受攻击的组件，并对易受攻击的组件进行优先测试以降低风险并确保车辆安全。该模型在 OpenPilot 上得到了评估，实验证明了 VSRQ 模型在识别车辆智能连接系统安全方面的有效性。

VSRQ：车联网智能系统安全风险的定量评估方法

VSRQ: Quantitative Assessment Method for Safety Risk of Vehicle  Intelligent Connected System

With the increasing need to strengthen vehicle safety and detection, the
availability of pre-existing methods of catching criminals and identifying
vehicles manually through the various traffic surveillance cameras is not only
time-consuming but also inefficient. With the advancement of technology in
every field the use of real-time traffic surveillance models will help
facilitate an easy approach. Keeping this in mind, the main focus of our paper
is to develop a combined face recognition and number plate recognition model to
ensure vehicle safety and real-time tracking of running-away criminals and
stolen vehicles.

本研究旨在开发一种结合人脸识别和车牌识别技术的模型，以保障车辆安全和实时追踪逃犯和被盗车辆。

利用车牌检测和面部特征识别进行可疑车辆检测

Suspicious Vehicle Detection Using Licence Plate Detection And Facial  Feature Recognition

One of the main paths towards the reduction of traffic accidents is the
increase in vehicle safety through driver assistance systems or even systems
with a complete level of autonomy. In these types of systems, tasks such as
obstacle detection and segmentation, especially the Deep Learning-based ones,
play a fundamental role in scene understanding for correct and safe navigation.
Besides that, the wide variety of sensors in vehicles nowadays provides a rich
set of alternatives for improvement in the robustness of perception in
challenging situations, such as navigation under lighting and weather adverse
conditions. Despite the current focus given to the subject, the literature
lacks studies on radar-based and radar-camera fusion-based perception. Hence,
this work aims to carry out a study on the current scenario of camera and
radar-based perception for ADAS and autonomous vehicles. Concepts and
characteristics related to both sensors, as well as to their fusion, are
presented. Additionally, we give an overview of the Deep Learning-based
detection and segmentation tasks, and the main datasets, metrics, challenges,
and open questions in vehicle perception.

本文研究了自动驾驶系统中车辆安全性的提高、深度学习基础的障碍物检测和分割、雷达传感器和摄像头传感器的融合，以及在车辆感知中的数据集、度量、挑战和开放性问题，这些元素对于提高自动驾驶系统的普及率具有重要意义。