Collaborative perception among multiple connected and autonomous vehicles can
greatly enhance perceptive capabilities by allowing vehicles to exchange
supplementary information via communications. Despite advances in previous
approaches, challenges still remain due to channel variations and data
heterogeneity among collaborative vehicles. To address these issues, we propose
ACC-DA, a channel-aware collaborative perception framework to dynamically
adjust the communication graph and minimize the average transmission delay
while mitigating the side effects from the data heterogeneity. Our novelties
lie in three aspects. We first design a transmission delay minimization method,
which can construct the communication graph and minimize the transmission delay
according to different channel information state. We then propose an adaptive
data reconstruction mechanism, which can dynamically adjust the rate-distortion
trade-off to enhance perception efficiency. Moreover, it minimizes the temporal
redundancy during data transmissions. Finally, we conceive a domain alignment
scheme to align the data distribution from different vehicles, which can
mitigate the domain gap between different vehicles and improve the performance
of the target task. Comprehensive experiments demonstrate the effectiveness of
our method in comparison to the existing state-of-the-art works.

通过通信交换补充信息的多个连接和自主车辆之间的协同感知可以极大地增强感知能力，我们提出了一种通道感知的协同感知框架 ACC-DA，通过动态调整通信图并减小平均传输延迟来解决通道变化和数据异构性所带来的挑战。

自主驾驶中领域对齐的协作感知中的自适应通信

Adaptive Communications in Collaborative Perception with Domain  Alignment for Autonomous Driving

The growing demand on high-quality and low-latency multimedia services has
led to much interest in edge caching techniques. Motivated by this, we in this
paper consider edge caching at the base stations with unknown content
popularity distributions. To solve the dynamic control problem of making
caching decisions, we propose a deep actor-critic reinforcement learning based
multi-agent framework with the aim to minimize the overall average transmission
delay. To evaluate the proposed framework, we compare the learning-based
performance with three other caching policies, namely least recently used
(LRU), least frequently used (LFU), and first-in-first-out (FIFO) policies.
Through simulation results, performance improvements of the proposed framework
over these three caching algorithms have been identified and its superior
ability to adapt to varying environments is demonstrated.

本文提出一种基于多智能体框架的深度强化学习方法以最小化整体平均传输延迟，通过与三种不同的缓存策略对比，结果显示出该算法对于不同环境的能力更强，性能也更好。