Cooperative intelligent transport systems rely on a set of
Vehicle-to-Everything (V2X) applications to enhance road safety. Emerging new
V2X applications like Advanced Driver Assistance Systems (ADASs) and Connected
Autonomous Driving (CAD) applications depend on a significant amount of shared
data and require high reliability, low end-to-end (E2E) latency, and high
throughput. However, present V2X communication technologies such as ITS-G5 and
C-V2X (Cellular V2X) cannot satisfy these requirements alone. In this paper, we
propose an intelligent, scalable hybrid vehicular communication architecture
that leverages the performance of multiple Radio Access Technologies (RATs) to
meet the needs of these applications. Then, we propose a communication mode
selection algorithm based on Deep Reinforcement Learning (DRL) to maximize the
network's reliability while limiting resource consumption. Finally, we assess
our work using the platooning scenario that requires high reliability.
Numerical results reveal that the hybrid vehicular communication architecture
has the potential to enhance the packet reception rate (PRR) by up to 30%
compared to both the static RAT selection strategy and the multi-criteria
decision-making (MCDM) selection algorithm. Additionally, it improves the
efficiency of the redundant communication mode by 20% regarding resource
consumption

合作智能交通系统依赖一套车到车（V2X）应用来提高道路安全。本文提出了一种智能可扩展的混合车载通信架构，利用多种无线接入技术的性能满足这些应用的需求，并基于深度强化学习提出一种通信模式选择算法来最大化网络可靠性并限制资源消耗，通过车队编队场景的数值结果验证了混合车载通信架构相比静态车载选择策略和多准则决策选择算法可将数据包接收率提高 30％以及资源消耗方面冗余通信模式效率提高 20％。

混合车载通信网络中基于强化学习的 RAT 选择

DRL-Based RAT Selection in a Hybrid Vehicular Communication Network

Accurate perception is essential for advancing autonomous driving and
addressing safety challenges in modern transportation systems. Despite
significant advancements in computer vision for object recognition, current
perception methods still face difficulties in complex real-world traffic
environments. Challenges such as physical occlusion and limited sensor field of
view persist for individual vehicle systems. Cooperative Perception (CP) with
Vehicle-to-Everything (V2X) technologies has emerged as a solution to overcome
these obstacles and enhance driving automation systems. While some research has
explored CP's fundamental architecture and critical components, there remains a
lack of comprehensive summaries of the latest innovations, particularly in the
context of V2X communication technologies. To address this gap, this paper
provides a comprehensive overview of the evolution of CP technologies, spanning
from early explorations to recent developments, including advancements in V2X
communication technologies. Additionally, a contemporary generic framework is
proposed to illustrate the V2X-based CP workflow, aiding in the structured
understanding of CP system components. Furthermore, this paper categorizes
prevailing V2X-based CP methodologies based on the critical issues they
address. An extensive literature review is conducted within this taxonomy,
evaluating existing datasets and simulators. Finally, open challenges and
future directions in CP for autonomous driving are discussed by considering
both perception and V2X communication advancements.

自动驾驶中准确的感知是推动该领域发展并解决安全挑战的关键。新兴的基于车联网技术的合作感知被提出作为一种解决方案，以克服当前感知方法在复杂真实交通环境中遇到的困难，并提升驾驶自动化系统的性能。本文通过全面综述合作感知技术的演进，并重点关注与车联网通信技术相关的最新研究，提出了一个当代通用框架来说明基于车联网的合作感知工作流程，分析了当前所解决的关键问题并进行了文献综述，最后探讨了自动驾驶中感知和车联网通信发展所面临的挑战和未来方向。