Vehicle environmental awareness is a crucial issue in improving road safety. Through a variety of sensors and vehicle-to-vehicle communication, vehicles can collect a wealth of data. However, to make these data useful, sensor data must be integrated effectively. This paper focuses on the integration of image data and vehicle-to-vehicle communication data. More specifically, our goal is to identify the locations of vehicles sending messages within images, a challenge termed the vehicle identification problem. In this paper, we employ a supervised learning model to tackle the vehicle identification problem. However, we face two practical issues: first, drivers are typically unwilling to share privacy-sensitive image data, and second, drivers usually do not engage in data labeling. To address these challenges, this paper introduces a comprehensive solution to the vehicle identification problem, which leverages federated learning and automatic labeling techniques in combination with the aforementioned supervised learning model. We have validated the feasibility of our proposed approach through experiments.

翻译：车辆环境感知是提升道路安全的关键问题。通过多种传感器与车车间通信技术，车辆能够采集丰富数据。然而，要使这些数据产生价值，必须对传感器数据进行有效整合。本文聚焦于图像数据与车车间通信数据的融合处理。具体而言，我们的目标是在图像中定位发送消息的车辆位置，这一挑战被称为车辆识别问题。本文采用监督学习模型解决车辆识别问题，但面临两个实际难题：其一，驾驶员通常不愿共享涉及隐私的图像数据；其二，驾驶员普遍不参与数据标注工作。为应对这些挑战，本文提出结合联邦学习与自动标注技术的综合解决方案，并将其与前述监督学习模型相融合。我们已通过实验验证了所提方法的可行性。