Collaborative perception in Internet of Vehicles (IoV) aggregates multi-vehicle observations for broader scene coverage and improved decision-making. However, fusion quality degrades under spatiotemporal heterogeneity from unsynchronized clocks, communication delays, and motion variations across vehicles. Prior work mitigates these through spatial transformations or fixed time-offset corrections, overlooking time-varying clock drifts and delays that cause persistent feature misalignment. To overcome these, we propose a spatiotemporal feature alignment and weighted fusion framework. Specifically, network synchronization is designed to continuously compensate for clock state differences between vehicles and establish a common time reference, onto which all feature timestamps can be mapped. After synchronization, to align the freshness of received features since their generation, their Age of Information (AoI) is determined by estimating network delay with given feature size and link quality. Our spatiotemporal feature alignment then projects vehicles' features into one spatial coordinate and corrects them to a synchronized fusion instant using AoIs, enabling all features to describe the scene coherently. Furthermore, due to varying synchronization and alignment quality, we estimate their uncertainties and integrate with AoI to generate feature weights for efficient fusion, prioritizing fresh, reliable feature regions. Simulations show consistent perception accuracy improvements over strong baselines under clock drifts and link delays.

翻译：车联网协同感知通过聚合多车辆观测数据，以扩展场景覆盖范围并提升决策性能。然而，由于车辆间时钟未同步、通信延迟及运动状态差异导致的时空异质性，会降低融合质量。现有研究主要通过空间变换或固定时间偏移校正来缓解上述问题，但忽略了时变时钟漂移与延迟所引起的持续特征失准。为克服这些局限，本文提出一种时空特征对齐与加权融合框架。具体而言，网络同步模块被设计用于持续补偿车辆间的时钟状态差异，并建立统一的时间基准，所有特征时间戳均可映射至该基准。同步后，为对齐接收特征自生成以来的新鲜度，通过结合给定特征大小与链路质量估计网络延迟，进而确定各特征的信息年龄。本框架的时空特征对齐模块随后将各车辆特征投影至统一空间坐标系，并利用信息年龄将其校正至同步融合时刻，使所有特征能够一致地描述场景。此外，鉴于同步与对齐质量存在差异，我们估计其不确定性并结合信息年龄生成特征权重，以实现高效融合，从而优先融合新鲜可靠的特征区域。仿真实验表明，在存在时钟漂移与链路延迟的条件下，本方法相较于现有强基线模型能持续提升感知精度。