Abstract:

Roadside 3D target detection provides wide-view traffic information， effectively enhancing single-vehicle autonomous driving. In general， deploying a perception system at a new intersection requires a large amount of data collection and manual labeling to ensure the detective accuracy of the training model， which is time-consuming and costly. For the above-mentioned problems， in this paper an unsupervised domain adaptation （UDA） algorithm is proposed for roadside application， which achieves efficient knowledge transfer and accurate 3D object detection by matching cross-domain spatiotemporal features between high-quality labeled roadside data and unknown roadside scenes. Firstly， a source-domain training model （RoadPillars） is constructed that balances cross-domain data distributions， effectively reducing overfitting of the model to the original data distribution and improving model generalization. Moreover， a cross-domain transfer scheme is designed that ensures spatial consistency of continuous sequences for stable and robust UDA. The experimental results on three distinct roadside scenes and LiDAR types from public datasets demonstrate that the unsupervised domain adaption algorithm proposed in this paper achieves an average accuracy improvement of 24.2% and 8.0% over state-of-the-art approach， significantly improving the generalization and reliability of roadside perception.

Key words: roadside perception, 3D object detection, transfer learning, unsupervised domain adaptation, feature matching