Collaborative perception is a cornerstone of intelligent connected vehicles, enabling them to share and integrate sensory data to enhance situational awareness. However, measuring the impact of high transmission delay and inconsistent delay on collaborative perception in real communication scenarios, as well as improving the effectiveness of collaborative perception under such conditions, remain significant challenges in the field. To address these challenges, we incorporate the key factor of information freshness into the collaborative perception mechanism and develop a model that systematically measures and analyzes the impacts of real-world communication on collaborative perception performance. This provides a new perspective for accurately evaluating and optimizing collaborative perception performance. We propose and validate an Age of Information (AoI)-based optimization framework that strategically allocates communication resources to effectively control the system's AoI, thereby significantly enhancing the freshness of information transmission and the accuracy of perception. Additionally, we introduce a novel experimental approach that comprehensively assesses the varying impacts of different types of delay on perception results, offering valuable insights for perception performance optimization under real-world communication scenarios.

翻译：协同感知是智能网联汽车的基石，使其能够共享并整合传感数据以增强态势感知能力。然而，在实际通信场景中，衡量高传输延迟与不一致延迟对协同感知的影响，并在此类条件下提升协同感知的有效性，仍是该领域面临的重大挑战。为应对这些挑战，我们将信息新鲜度这一关键因素纳入协同感知机制，并开发了一个系统性地测量与分析实际通信对协同感知性能影响的模型。这为准确评估与优化协同感知性能提供了新的视角。我们提出并验证了一种基于信息年龄的优化框架，该框架通过策略性地分配通信资源，有效控制系统信息年龄，从而显著提升信息传输的新鲜度与感知准确性。此外，我们引入了一种新颖的实验方法，全面评估不同类型延迟对感知结果的不同影响，为实际通信场景下的感知性能优化提供了有价值的见解。