FuseRoad: Enhancing Lane Shape Prediction Through Semantic Knowledge Integration and Cross-Dataset Training

The rapid evolution of advanced driver assistance systems (ADAS) has been driven by the advances of deep neural networks, and multi-tasking is essential for autonomous driving systems. This paper presents FuseRoad, a new multi-task model that leverages cross-dataset learning to address the dependency on specific multi-task datasets and reduce the annotation costs. It integrates semantic segmentation and lane detection into an end-to-end framework while providing an effective approach to utilize multiple single-task datasets. By incorporating Semantic Road Knowledge Extractor (SRKE) to direct more attentions on the roadway, FuseRoad enhances the accuracy and reliability of lane detection. The model also employs the logit normalization loss to address the issue of overconfidence commonly faced by conventional lane detection methods. In experiments, FuseRoad outperforms state-of-the-art approaches in both accuracy and F-1 score. The evaluation on semantic segmentation metrics also demonstrates that the proposed technique is highly effective for multi-task road scene analysis. Code and datasets are available at https://github.com/HengChihHsiao/FuseRoad.