Perception sensors, particularly camera and Lidar, are key elements of
Autonomous Driving Systems (ADS) that enable them to comprehend their
surroundings for informed driving and control decisions. Therefore, developing
realistic camera and Lidar simulation methods, also known as camera and Lidar
models, is of paramount importance to effectively conduct simulation-based
testing for ADS. Moreover, the rise of deep learning-based perception models
has propelled the prevalence of perception sensor models as valuable tools for
synthesising diverse training datasets. The traditional sensor simulation
methods rely on computationally expensive physics-based algorithms,
specifically in complex systems such as ADS. Hence, the current potential
resides in learning-based models, driven by the success of deep generative
models in synthesising high-dimensional data. This paper reviews the current
state-of-the-art in learning-based sensor simulation methods and validation
approaches, focusing on two main types of perception sensors: cameras and
Lidars. This review covers two categories of learning-based approaches, namely
raw-data-based and object-based models. Raw-data-based methods are explained
concerning the employed learning strategy, while object-based models are
categorised based on the type of error considered. Finally, the paper
illustrates commonly used validation techniques for evaluating perception
sensor models and highlights the existing research gaps in the area.

该论文回顾了基于学习的传感器模拟方法和验证方法的最新研究进展，重点关注相机和激光雷达这两种主要类型的感知传感器，并概述了评估感知传感器模型的常用验证技术和目前研究领域中存在的研究缺口。

无人驾驶系统中基于学习的相机和激光雷达模拟方法综述

Review of the Learning-based Camera and Lidar Simulation Methods for  Autonomous Driving Systems

Smart intersections have the potential to improve road safety with sensing,
communication, and edge computing technologies. Perception sensors installed at
a smart intersection can monitor the traffic environment in real time and send
infrastructure-based warnings to nearby travelers through V2X communication.
This paper investigated how infrastructure-based warnings can influence driving
behaviors and improve roundabout safety through a driving-simulator study - a
challenging driving scenario for human drivers. A co-simulation platform
integrating Simulation of Urban Mobility (SUMO) and Webots was developed to
serve as the driving simulator. A real-world roundabout in Ann Arbor, Michigan
was built in the co-simulation platform as the study area, and the merging
scenarios were investigated. 36 participants were recruited and asked to
navigate the roundabout under three danger levels (e.g., low, medium, high) and
three collision warning designs (e.g., no warning, warning issued 1 second in
advance, warning issued 2 seconds in advance). Results indicated that advanced
warnings can significantly enhance safety by minimizing potential risks
compared to scenarios without warnings. Earlier warnings enabled smoother
driver responses and reduced abrupt decelerations. In addition, a personalized
intention prediction model was developed to predict drivers' stop-or-go
decisions when the warning is displayed. Among all tested machine learning
models, the XGBoost model achieved the highest prediction accuracy with a
precision rate of 95.56% and a recall rate of 97.73%.

通过驾驶模拟器研究，探讨了基于基础设施的警告系统在改善交通安全和减少事故风险方面的作用，结果显示提前发出警告可以显著提高安全性。

基于基础设施的警告系统对驾驶行为的评估 —— 环形交叉口研究

Evaluation of Infrastructure-based Warning System on Driving Behaviors-A  Roundabout Study

Radar is a key component of the suite of perception sensors used for safe and
reliable navigation of autonomous vehicles. Its unique capabilities include
high-resolution velocity imaging, detection of agents in occlusion and over
long ranges, and robust performance in adverse weather conditions. However, the
usage of radar data presents some challenges: it is characterized by low
resolution, sparsity, clutter, high uncertainty, and lack of good datasets.
These challenges have limited radar deep learning research. As a result,
current radar models are often influenced by lidar and vision models, which are
focused on optical features that are relatively weak in radar data, thus
resulting in under-utilization of radar's capabilities and diminishing its
contribution to autonomous perception. This review seeks to encourage further
deep learning research on autonomous radar data by 1) identifying key research
themes, and 2) offering a comprehensive overview of current opportunities and
challenges in the field. Topics covered include early and late fusion,
occupancy flow estimation, uncertainty modeling, and multipath detection. The
paper also discusses radar fundamentals and data representation, presents a
curated list of recent radar datasets, and reviews state-of-the-art lidar and
vision models relevant for radar research. For a summary of the paper and more
results, visit the website: autonomous-radars.github.io.

该研究综述雷达在自主驾驶车辆中的应用及挑战，提出了针对雷达数据的深度学习研究的关键主题，包括早期和晚期融合，占用流估计，不确定性建模和多径检测等，以及相关的雷达数据集、目前最先进的激光雷达和视觉模型等。

自动驾驶雷达：深度学习方法与挑战综述

Radars for Autonomous Driving: A Review of Deep Learning Methods and  Challenges

Multi-modal fusion is a fundamental task for the perception of an autonomous
driving system, which has recently intrigued many researchers. However,
achieving a rather good performance is not an easy task due to the noisy raw
data, underutilized information, and the misalignment of multi-modal sensors.
In this paper, we provide a literature review of the existing multi-modal-based
methods for perception tasks in autonomous driving. Generally, we make a
detailed analysis including over 50 papers leveraging perception sensors
including LiDAR and camera trying to solve object detection and semantic
segmentation tasks. Different from traditional fusion methodology for
categorizing fusion models, we propose an innovative way that divides them into
two major classes, four minor classes by a more reasonable taxonomy in the view
of the fusion stage. Moreover, we dive deep into the current fusion methods,
focusing on the remaining problems and open-up discussions on the potential
research opportunities. In conclusion, what we expect to do in this paper is to
present a new taxonomy of multi-modal fusion methods for the autonomous driving
perception tasks and provoke thoughts of the fusion-based techniques in the
future.

本文针对自动驾驶感知任务中存在的多模态融合问题进行文献综述，分析超过 50 篇包括 LiDAR 和相机在内的感知传感器解决目标检测和语义分割任务的方法，并提出一种更合理的分类方法。