The segmentation of road markings plays a crucial role in visual perception for the autonomous driving system. It enables vehicles to recognize road markings at the pixel-level, and facilitates subsequent path planning, localization, and map construction tasks. Current techniques mainly focus on normal driving scenes (i.e., clear daytime), and the performance would decrease significantly for adverse weather conditions. This work proposes RMSeg-UDA: an unsupervised domain adaptive road marking segmentation framework. By combining schedule self-training and class-conditioned adversarial training, the network utilizes both labeled normal data and unlabeled data from other domains to train a road marking segmentation model. For the evaluation on adverse conditions, a new image dataset, RLMDAC, is established with rainy and nighttime driving scenes. The experiments conducted using both public and our datasets have demonstrated the effectiveness of the proposed technique. Code and dataset are available at https://github.com/stu9113611/RMSeg-UDA.

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.

Multivariate time-series data in numerous real-world applications (e.g., healthcare and industry) are informative but challenging due to the lack of labels and high dimensionality. Recent studies in self-supervised learning have shown their potential in learning rich representations without relying on labels, yet they fall short in learning disentangled embeddings and addressing issues of inductive bias (e.g., transformation-invariance). To tackle these challenges, we propose TimeDRL, a generic multivariate time-series representation learning frame-work with disentangled dual-level embeddings. TimeDRL is characterized by three novel features: (i) disentangled derivation of timestamp-level and instance-level embeddings from patched time-series data using a [CLS] token strategy; (ii) utilization of timestamp-predictive and instance-contrastive tasks for disentangled representation learning, with the former optimizing timestamp-level embeddings with predictive loss, and the latter optimizing instance-level embeddings with contrastive loss; and (iii) avoidance of augmentation methods to eliminate inductive biases, such as transformation-invariance from cropping and masking. Comprehensive experiments on 6 time-series forecasting datasets and 5 time-series classification datasets have shown that TimeDRL consistently surpasses existing representation learning approaches, achieving an average improvement of forecasting by 58.02% in MSE and classification by 1.48% in accuracy. Further-more, extensive ablation studies confirmed the relative contribution of each component in TimeDRL's architecture, and semi-supervised learning evaluations demonstrated its effectiveness in real-world scenarios, even with limited labeled data. The code is available at https://github.com/blacksnail789521/TimeDRL.