计算机科学 ›› 2025, Vol. 52 ›› Issue (11A): 241100009-6.doi: 10.11896/jsjkx.241100009
王睿家1, 申振2, 李俊杰2, 丁磊1,2
WANG Ruijia1, SHEN Zhen2, LI Junjie2, DING Lei1,2
摘要： 自动驾驶车辆(Connected and Autonomous Vehicles,CAVs)利用车联万物(Vehicle-to-Everything,V2X)和6G网络数据实现合作感知服务(Cooperative Perception Service,CPS)。在实际交通系统中,会出现多个CAVs同时感知并分享同一对象的情况,导致在网络中交换了许多不相关的冗余信息,从而增加了额外的通信开销。为了解决这个问题,提出了一种基于信息价值(Value of Information,VoI)的冗余压缩策略。首先,通过数学方法来量化感知信息的价值;接着,当CAV向基站发送上传请求时,信息价值汇总到基站;然后,将CPS的满意度表示为基站控制下的一个最大化问题,并通过模拟退火(Simulated Annealing,SA)算法进行求解。该策略允许基站最优地控制CAV上传的信息,最大限度地提高CAV协作感知的效用,并最小化V2X网络中的冗余。仿真结果表明,与现有策略相比,该策略能有效降低目标冗余,使平均减少22.3%的传输延迟,使CPS质量提高21.6%。
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