Autonomous Vehicle (AV) technology is advancing rapidly, promising a
significant shift in road transportation safety and potentially resolving
various complex transportation issues. With the increasing deployment of AVs by
various companies, questions emerge about how AVs interact with each other and
with human drivers, especially when AVs are prevalent on the roads. Ensuring
cooperative interaction between AVs and between AVs and human drivers is
critical, though there are concerns about possible negative competitive
behaviors. This paper presents a multi-stage approach, starting with the
development of a single AV and progressing to connected AVs, incorporating
sharing and caring V2V communication strategy to enhance mutual coordination. A
survey is conducted to validate the driving performance of the AV and will be
utilized for a mixed traffic case study, which focuses on how the human drivers
will react to the AV driving alongside them on the same road. Results show that
using deep reinforcement learning, the AV acquired driving behavior that
reached human driving performance. The adoption of sharing and caring based V2V
communication within AV networks enhances their driving behavior, aids in more
effective action planning, and promotes collaborative behavior amongst the AVs.
The survey shows that safety in mixed traffic cannot be guaranteed, as we
cannot control human ego-driven actions if they decide to compete with AV.
Consequently, this paper advocates for enhanced research into the safe
incorporation of AVs on public roads.

自动驾驶汽车技术快速发展，对道路交通安全产生了重大影响，同时也解决了多种复杂的交通问题。为了确保自动驾驶汽车之间以及自动驾驶汽车与人类驾驶员之间的合作互动，本研究提出了一个多阶段的方法，采用共享和关怀的车对车通信策略来加强协同行为。通过进行一项调查来验证自动驾驶汽车的驾驶性能，并可用于一个混合交通案例研究，重点研究人类驾驶员如何对待与他们同时驾驶在同一条道路上的自动驾驶汽车。研究结果表明，采用深度强化学习，自动驾驶车辆获得了接近人类驾驶性能的驾驶行为。在自动驾驶汽车网络中采用共享和关怀的车对车通信可以提高其驾驶行为，促进更有效的行动计划，并增进自动驾驶汽车之间的合作行为。调查结果显示，无法保证混合交通中的安全性，因为我们无法控制人类驾驶员出于自我的行为，如果他们决定与自动驾驶汽车竞争。因此，本研究倡导加强对自动驾驶汽车在公共道路上安全融入的研究。

自动驾驶车辆在全自动和混合交通条件下的开发与评估

Development and Assessment of Autonomous Vehicles in Both Fully  Automated and Mixed Traffic Conditions

Accurately aligning millimeter-wave (mmWave) and terahertz (THz) narrow beams
is essential to satisfy reliability and high data rates of 5G and beyond
wireless communication systems. However, achieving this objective is difficult,
especially in vehicle-to-vehicle (V2V) communication scenarios, where both
transmitter and receiver are constantly mobile. Recently, additional sensing
modalities, such as visual sensors, have attracted significant interest due to
their capability to provide accurate information about the wireless
environment. To that end, in this paper, we develop a deep learning solution
for V2V scenarios to predict future beams using images from a 360 camera
attached to the vehicle. The developed solution is evaluated on a real-world
multi-modal mmWave V2V communication dataset comprising co-existing 360 camera
and mmWave beam training data. The proposed vision-aided solution achieves
$\approx 85\%$ top-5 beam prediction accuracy while significantly reducing the
beam training overhead. This highlights the potential of utilizing vision for
enabling highly-mobile V2V communications.

该研究论文利用深度学习方法，在车辆之间的通信场景中，通过车载 360 度相机捕获的图像来预测未来的波束，实现了高达 85% 的前 5 个波束预测准确率，并显著减少波束训练的开销，展示了利用视觉实现高度移动车辆之间通信的潜力。

车载摄像头引导毫米波束：方法与实际车车通信示范

Vehicle Cameras Guide mmWave Beams: Approach and Real-World V2V  Demonstration

Bird's eye view (BEV) semantic segmentation plays a crucial role in spatial
sensing for autonomous driving. Although recent literature has made significant
progress on BEV map understanding, they are all based on single-agent
camera-based systems. These solutions sometimes have difficulty handling
occlusions or detecting distant objects in complex traffic scenes.
Vehicle-to-Vehicle (V2V) communication technologies have enabled autonomous
vehicles to share sensing information, dramatically improving the perception
performance and range compared to single-agent systems. In this paper, we
propose CoBEVT, the first generic multi-agent multi-camera perception framework
that can cooperatively generate BEV map predictions. To efficiently fuse camera
features from multi-view and multi-agent data in an underlying Transformer
architecture, we design a fused axial attention module (FAX), which captures
sparsely local and global spatial interactions across views and agents. The
extensive experiments on the V2V perception dataset, OPV2V, demonstrate that
CoBEVT achieves state-of-the-art performance for cooperative BEV semantic
segmentation. Moreover, CoBEVT is shown to be generalizable to other tasks,
including 1) BEV segmentation with single-agent multi-camera and 2) 3D object
detection with multi-agent LiDAR systems, achieving state-of-the-art
performance with real-time inference speed. The code is available at
this https URL

本文提出了 CoBEVT，这是一个通用的多代理多摄像头感知框架，能够协同生成 BEV 地图预测。在 V2V 感知数据集 OPV2V 上的广泛实验表明，CoBEVT 实现了合作 BEV 语义分割的最先进性能。此外，CoBEVT 还可应用于其他任务，包括单代理多摄像头的 BEV 分割和多代理 LiDAR 系统的 3D 对象检测，实现了实时推理速度和最先进的性能。