Traffic signal control (TSC) is crucial for reducing traffic congestion that
leads to smoother traffic flow, reduced idling time, and mitigated CO2
emissions. In this study, we explore the computer vision approach for TSC that
modulates on-road traffic flows through visual observation. Unlike traditional
feature-based approaches, vision-based methods depend much less on heuristics
and predefined features, bringing promising potentials for end-to-end learning
and optimization of traffic signals. Thus, we introduce a holistic traffic
simulation framework called TrafficDojo towards vision-based TSC and its
benchmarking by integrating the microscopic traffic flow provided in SUMO into
the driving simulator MetaDrive. This proposed framework offers a versatile
traffic environment for in-depth analysis and comprehensive evaluation of
traffic signal controllers across diverse traffic conditions and scenarios. We
establish and compare baseline algorithms including both traditional and
Reinforecment Learning (RL) approaches. This work sheds insights into the
design and development of vision-based TSC approaches and open up new research
opportunities. All the code and baselines will be made publicly available.

该研究探讨了基于计算机视觉的交通信号控制方法，通过视觉观测调节道路上的交通流量，引入了名为 TrafficDojo 的综合交通模拟框架，通过将 SUMO 提供的微观交通流量集成到驾驶模拟器 MetaDrive 中进行基线算法的比较和综合评估，为设计和发展基于视觉的交通信号控制方法打开了新的研究机会。

基于视觉的交通信号控制及微观仿真的整体框架

A Holistic Framework Towards Vision-based Traffic Signal Control with  Microscopic Simulation

Data-driven approaches have been proven effective in solving combinatorial
optimization problems over graphs such as the traveling salesman problems and
the vehicle routing problem. The rationale behind such methods is that the
input instances may follow distributions with salient patterns that can be
leveraged to overcome the worst-case computational hardness. For optimization
problems over graphs, the common practice of neural combinatorial solvers
consumes the inputs in the form of adjacency matrices. In this paper, we
explore a vision-based method that is conceptually novel: can neural models
solve graph optimization problems by \textit{taking a look at the graph
pattern}? Our results suggest that the performance of such vision-based methods
is not only non-trivial but also comparable to the state-of-the-art
matrix-based methods, which opens a new avenue for developing data-driven
optimization solvers.

基于视觉方法的神经模型解决图优化问题的性能与矩阵方法相当，为发展数据驱动优化求解器开辟了新的途径。

VN-Solver: 基于视觉的图像组合优化神经求解器

VN-Solver: Vision-based Neural Solver for Combinatorial Optimization  over Graphs

Single frame data contains finite information which limits the performance of
the existing vision-based multi-camera 3D object detection paradigms. For
fundamentally pushing the performance boundary in this area, a novel paradigm
dubbed BEVDet4D is proposed to lift the scalable BEVDet paradigm from the
spatial-only 3D space to the spatial-temporal 4D space. We upgrade the naive
BEVDet framework with a few modifications just for fusing the feature from the
previous frame with the corresponding one in the current frame. In this way,
with negligible additional computing budget, we enable BEVDet4D to access the
temporal cues by querying and comparing the two candidate features. Beyond
this, we simplify the task of velocity prediction by removing the factors of
ego-motion and time in the learning target. As a result, BEVDet4D with robust
generalization performance reduces the velocity error by up to -62.9%. This
makes the vision-based methods, for the first time, become comparable with
those relied on LiDAR or radar in this aspect. On challenge benchmark nuScenes,
we report a new record of 54.5% NDS with the high-performance configuration
dubbed BEVDet4D-Base, which surpasses the previous leading method BEVDet-Base
by +7.3% NDS. The source code is publicly available for further research at
this https URL .

提出一种名为 BEVDet4D 的新型监测方法，将视觉单帧数据提升至时空 4D 空间，通过对前一帧和当前帧候选特征的对比，使 BEVDet4D 能够访问时间线索，已达到与雷达检测方法相媲美的检测表现

BEVDet4D: 多相机 3D 物体检测中的时序线索利用

BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection

Intraoperative segmentation and tracking of minimally invasive instruments is
a prerequisite for computer- and robotic-assisted surgery. Since additional
hardware like tracking systems or the robot encoders are cumbersome and lack
accuracy, surgical vision is evolving as promising techniques to segment and
track the instruments using only the endoscopic images. However, what is
missing so far are common image data sets for consistent evaluation and
benchmarking of algorithms against each other. The paper presents a comparative
validation study of different vision-based methods for instrument segmentation
and tracking in the context of robotic as well as conventional laparoscopic
surgery. The contribution of the paper is twofold: we introduce a comprehensive
validation data set that was provided to the study participants and present the
results of the comparative validation study. Based on the results of the
validation study, we arrive at the conclusion that modern deep learning
approaches outperform other methods in instrument segmentation tasks, but the
results are still not perfect. Furthermore, we show that merging results from
different methods actually significantly increases accuracy in comparison to
the best stand-alone method. On the other hand, the results of the instrument
tracking task show that this is still an open challenge, especially during
challenging scenarios in conventional laparoscopic surgery.

本文介绍了一项比较研究，探讨了在机器人以及传统腹腔镜手术中，基于视觉的不同方法用于器械分割和追踪的效果。研究表明，现代的深度学习方法在器械分割任务中表现优异，但结果还不完美。此外，合并不同方法的结果实际上显著提高了准确性。与此同时，器械追踪任务的结果表明，在传统腹腔镜手术中这仍然是一个待解决的挑战。