In this paper, we explore the use of cooperative perception to improve the perception performance of autonomous vehicles. By aggregating perception data from multiple nearby vehicles and roadside units, we can see through obstructions, detect objects at a distance, and improve detection accuracy. We propose a new cooperative perception framework, V2X-HAN. This framework mainly uses a heterogeneous graph attention network model, which can better capture the complex structure and rich information in the heterogeneous graph, achieve better feature fusion, and thus improve the accuracy of detection. We trained and validated the OPV2V and V2XSet datasets, and compared them with related models. Many experimental results show that V2X-HAN has achieved good detection results in cooperative perception.
Mobile edge computing (MEC) is a promising paradigm for offloading compute-intensive services on vehicles to alleviate the problem of limited resources in the vehicles themselves. However, since the vehicle network involves multiple edge servers, MEC is facing the dilemma of how to fully utilize the edge resources to achieve the maximum benefit. In this paper, we aim to analyze MEC task offloading strategies from a multi-objective optimization perspective by considering independent partitionable computational tasks, and an MEC communication and computation offloading framework is constructed. A partial computational resource optimization (PCRO) algorithm is proposed, which jointly considers computational resource allocation and unit price adjustment to achieve minimum cost for vehicle users and maximum profit for edge servers. Extensive experimental results demonstrate the effectiveness of our proposed PCRO algorithm.
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