Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving

Collaborative perception has recently gained significant attention in autonomous driving, improving perception quality by enabling the exchange of additional information among vehicles. However, deploying collaborative perception systems can lead to domain shifts due to diverse environmental conditions and data heterogeneity among connected and autonomous vehicles (CAVs). To address these challenges, we propose a unified domain generalization framework applicable in both training and inference stages of collaborative perception. In the training phase, we introduce an Amplitude Augmentation (AmpAug) method to augment low-frequency image variations, broadening the model's ability to learn across various domains. We also employ a meta-consistency training scheme to simulate domain shifts, optimizing the model with a carefully designed consistency loss to encourage domain-invariant representations. In the inference phase, we introduce an intra-system domain alignment mechanism to reduce or potentially eliminate the domain discrepancy among CAVs prior to inference. Comprehensive experiments substantiate the effectiveness of our method in comparison with the existing state-of-the-art works. Code will be released at https://github.com/DG-CAVs/DG-CoPerception.git

Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving

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.Comment: 6 pages, 6 figure

Age of information in energy harvesting aided massive multiple access networks

Given the proliferation of the massive machine typecommunication devices (MTCDs) in beyond 5G (B5G) wirelessnetworks, energy harvesting (EH) aided next generation multipleaccess (NGMA) systems have drawn substantial attention inthe context of energy-efficient data sensing and transmission.However, without adaptive time slot (TS) and power allocationschemes, NGMA systems relying on stochastic sampling instantsmight lead to tardy actions associated both with high age ofinformation (AoI) as well as high power consumption. Formitigating the energy consumption, we exploit a pair of sleep scheduling policies, namely the multiple vacation (MV) policyand start-up threshold (ST) policy, which are characterized in thecontext of three typical multiple access protocols, including time division multiple access (TDMA), frequency-division multipleaccess (FDMA) and non-orthogonal multiple access (NOMA).Furthermore, we derive closed-form expressions for the MTCDsystem’s peak AoI, which are formulated as the optimizationobjective under the constraints of EH power, status update rateand stability conditions. An exact linear search based algorithmis proposed for finding the optimal solution by fixing the statusupdate rate. As a design alternative, a low complexity concave convex procedure (CCP) is also formulated for finding a near optimal solution relying on the original problem’s transformationinto a form represented by the difference of two convex problems.Our simulation results show that the proposed algorithms arebeneficial in terms of yielding a lower peak AoI at a low powerconsumption in the context of the multiple access protocolsconsidered.<br/

Edge intelligence for mission-critical 6G services in space-air-ground integrated networks

Next-generation wireless services will change our daily lives by supporting smart factories, intelligent transportation, augmented/virtual reality (AR/VR), etc. These sophisticated services are usually both data- and computation-intensive and must meet stringent latency and reliability requirements, which cannot be readily satisfied by cloud-based service processing. Therefore, the 6G cellular network is expected to jointly optimize communication, computing, caching and control. A further aspiration of 6G is to conceive a seamless space-air-ground integrated network (SAGIN) for filling the vast coverage holes across the globe, which brings about new opportunities for mission critical services. Therefore, in this article, we aim for conceiving ultra-reliable and low-latency edge intelligence (URLLEI) for supporting mission-critical services by harnessing the diversified communication, computing, and caching resources at the network edge of SAGIN. We critically appraise a number of key enabling techniques, including a number of new service-centric resource allocation techniques. Finally, a range of open challenges is discussed