Global Navigation Satellite System (GNSS) environment recognition is important for enhancing positioning reliability and context awareness in complex urban and natural scenes. However, existing methods predominantly rely on signal features and necessitate extensive labeled datasets, compromising their robustness in label-scarce scenarios. To address this, we propose a large language model (LLM)-assisted, few-sample GNSS environment recognition framework. Our approach leverages LLM-generated physical-semantic soft labels for knowledge distillation during training and incorporates a lightweight test-time adaptation (TTA) strategy during inference. Experimental results demonstrate that with only 1% of labeled training data, the proposed method outperforms the baseline by 6% in classification accuracy.
To address the challenges of insufficient global semantic modeling and blurred boundaries in urban traffic scene segmentation, this study proposes a frequency-spatial collaborative framework based on DeepLabV3+. A Spectral Decoupling Adaptive Modulation (SDAM) module enhances low-frequency semantics and high-frequency details in the frequency domain. A Hierarchical Spatial Dependency Modeling (HSDM) module captures local consistency and global semantic dependencies, while a Structure-guided Adaptive Multi-scale Fusion (SAMF) module dynamically integrates multi-scale features using structural priors. Experiments on Cityscapes and CamVid demonstrate improvements of 3.6% and 1.6% over DeepLabV3+, respectively, while maintaining real-time performance.
Weiwei Zhao, Yingchao Dong, Lingchao Wang et al.· International Journal of Adv...· 0 citations
This paper develops the first Vision-Language Model (VLM)-based framework for GNSS spoofing detection for autonomous vehicles by fusing front-camera visual data with in-vehicle sensor readings against GNSS-derived maneuvers.
Mohammed Aldeen, Muhammad Sami Irfan, Sagar Dasgupta et al.· arXiv.org· 0 citations
GPS signal outages present a fundamental challenge for low-cost integrated navigation systems, leading to unbounded error growth in the Inertial Navigation System (INS). Although the traditional filtering techniques provide a substantial Performance enhancement in GPS-INS integration, it fails at signal loss conditions. While machine-learning-based (ML-based) compensation methods have been explored, they often fail to model the complex, long-term error dynamics of MEMS sensors and face limitations in real-world applicability due to high computational costs. To mitigate these limitations, a DeGIN (Deep GPS Increment Network) is proposed, which is a hybrid CNN-BiLSTM architecture for predicting GNSS position correction increments in latitude and longitude during signal outages. The CNN module learns discriminative features from raw Inertial Measurement Unit (IMU) measurements, while the BiLSTM captures long-range temporal dependencies in both forward and backward directions, addressing the limitations of strictly unidirectional recurrent models. A key contribution is the explicit inclusion of an outage timer that conditions the predictions on the elapsed duration of signal loss. In addition, a hybrid direction-aware loss function is introduced to promote physically plausible trajectory estimates. Rigorous cross-domain experiments on multiple real-world public datasets demonstrate that the proposed model achieves higher accuracy and better generalization than conventional baselines and recent deep learning approaches. The model was trained on the NaveGo benchmark dataset and validated on an unseen held-out split, as well as on three entirely separate datasets: Nav200 (collected by the authors), INSANE (a published benchmark flight dataset), and simulated data generated by our simulation framework—all without any re-training or fine-tuning. On real-world data, DeGIN achieves a 64.2–86.4% improvement in position accuracy over recent deep learning baselines, with gains reaching up to 98.92% in aerial flight outage scenarios, and reduces error by 95.2–99.9% compared to EKF dead reckoning. Additionally, the framework is accompanied by a detailed optimization pathway, supporting its suitability for deployment on resource-constrained edge hardware. Overall, the proposed solution provides a robust and deployable approach for maintaining reliable navigation in GPS-denied environments.
Khalid M. Nasr, Sherif Mostafa, Ali Maher et al.· GPS Solutions· 0 citations
In urban canyons, multipath and non-line-of-sight reception introduce substantial pseudorange biases that degrade smartphone Global Navigation Satellite System (GNSS) positioning. To improve practical training efficiency beyond the multilayer perceptron (MLP)-based PrNet, we develop a hybrid model combining a convolutional neural network (CNN) and a long short-term memory (LSTM) network. The CNN extracts local features from GNSS observations, while the LSTM models temporal dependencies in pseudorange errors. The CNN-LSTM model was evaluated on the Google Smartphone Decimeter Challenge 2021 (GSDC 2021) dataset against weighted least squares (WLS), PrNet, CNN, and LSTM under consistent conditions. Autocorrelation and ablation analyses indicated temporal dependence in pseudorange residuals and complementary contributions from the CNN and LSTM components. Compared with PrNet, CNN-LSTM reduced the median epoch time by 30.1%, increased training throughput by 43.1%, and reduced graphics processing unit (GPU) training-step latency by 33.9%. For the overall trajectory, CNN-LSTM achieved a horizontal positioning root mean square error (RMSE) of 4.638 m, 5.40% lower than PrNet, while reducing the mean absolute error (MAE) and GSDC score by 9.39% and 5.43%, respectively. It also achieved the lowest values for all three metrics in urban interchange, building-obstructed, and open-sky environments, with improvements of 53.13%–73.96% over WLS. These results show that CNN-LSTM provides a better balance between positioning accuracy and practical GPU training efficiency than PrNet under the evaluated conditions.
Qiang Guo, Zeng-Ke Li, Meng Sun et al.· Measurement science and tech...· 0 citations
A visual foundation model-based multilabel perception framework that leverages existing on-board surveillance videos without requiring additional sensors or manual annotation is proposed, enabling zero-shot recognition of diverse environmental elements.
Shize Huang, Yimin Shen, Qianhui Fan et al.· Journal of Transportation En...· 0 citations
A lightweight object detection framework, termed MDCF-YOLO, which achieves a superior accuracy-efficiency trade-off compared to state-of-the-art lightweight object detectors and exhibits similarly competitive performance on the AI-TOD dataset, further validating its effectiveness and generalization capability in UAV remote sensing scenarios.
Peng-Fei Dai, Liang Chen, Ting Fan et al.· Cluster Computing· 1 citation
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