Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 3142-3149· 0 citations· 31 references
Abstract
Unmanned aerial vehicles (UAVs) are used for a wide range of tasks, many of which are autonomous. Since UAVs operate in three-dimensional space, visual data presents a rich source of input for them. Meanwhile, the rapid development of artificial intelligence has given rise to vision-language models (VLMs). These models not only recognize objects in a scene, as traditional computer vision techniques do, but also demonstrate a strong understanding of scene context, enabled by large language models (LLMs) serving as one of their core components. However, applications as specialized as aerial robotics typically require custom-trained models, which demand significant computational resources, large datasets, and expensive hardware. Motivated by these limitations, we investigate whether a state-of-the-art, general-purpose VLM can be leveraged for UAV-relevant tasks through carefully designed prompts, without additional training. We evaluate the VLM’s understanding of aerial scenes through the task of image captioning, using visual question answering that targets spatial information within the scene. Furthermore, we utilize VLMs to automatically generate metadata for aerial datasets, enabling more complex future tasks. Results show that VLMs can generate accurate metadata with high agreement with human-annotated data even without retraining. They also demonstrate that careful prompt engineering enhances the model’s ability to reason about and attend to spatial information, highlighting its potential in aerial robotics applications.
This work proposes ARIES-Mission2, a zero-shot Vision-Language-Action (VLA) framework that decouples visual-semantic perception from physical route optimization in low-altitude Unmanned Aerial Vehicle (UAV) mission generation and indicates that the TSP module maintains lower growth in computation time as the number of targets increases.
Junhao Wei, Yanxiao Li, Haochen Li et al.· 0 citations
Abstract. Visual Grounding (VG) is a core multimodal task that localizes image targets via natural language descriptions, and it is crucial for Unmanned Aerial Vehicle (UAV) applications. However, existing remote sensing (RS) VG datasets primarily rely on rule-driven explicit descriptions, which are inconsistent with real-world demands for interpreting implicit descriptions based on context, common sense, or domain knowledge. In addition, the cross-lingual robustness of Large Vision-Language Models (LVLMs) in implicit VG remains to be thoroughly investigated. This study evaluates the cross-lingual performance of Qwen2.5-VL-7B and InternVL3.5-8B across nine languages, incorporating analyses of text length dynamics, visual attention, and language structural effects. The results demonstrate that Qwen2.5-VL-7B exhibits outstanding performance in maintaining consistent task paradigm alignment (explicit VG outperforms implicit VG) and balanced text output, which benefits from the syntactic stability and low cognitive load of East Asian languages. In contrast, InternVL3.5-8B presents task paradigm misalignment, uncontrolled text expansion, and generative hallucinations. Furthermore, differences in language structures: East Asian languages depend on word order for semantic expression, whereas Western languages feature complex lexical morphology, significantly affect attention allocation and VG accuracy. This study provides key insights for optimizing cross-lingual vision-language alignment of LVLMs and advancing practical multimodal applications in UAV scenarios.
Jue Chen, Penghui Huang, Ran Ding et al.· The International Archives o...· 0 citations
Vision-language models (VLMs) are emerging as a key component of embodied intelligence, with growing applications in auto-labeling and end-to-end autonomous driving. However, existing approaches for improving spatiotemporal reasoning in VLMs often rely on complex preprocessing pipelines, expensive human annotations, or synthetic data, which limit scalability and introduce potential sim-to-real gaps. Moreover, although these methods have improved spatiotemporal understanding, they still lack strong metric reasoning capabilities for dynamic scenes, such as estimating object motion in real-world units. Prior work has explored LiDAR-based metric depth supervision to enhance spatial perception, but it does not directly address temporal reasoning. We introduce STAR-VLM, an automotive radar-supervised framework that enhances spatiotemporal VLMs with motion reasoning and metric velocity estimation for autonomous driving. Automotive radar is a low-cost and widely deployed sensor that provides complementary spatiotemporal supervision through range and Doppler measurements. By leveraging these measurements as label-free ground truth during training, STAR-VLM improves the metric spatiotemporal reasoning ability of VLMs. Through experiments on driving scenarios, we show that STAR-VLM achieves state-of-the-art performance on both motion classification and metric velocity estimation, outperforming even task-specific methods designed for each task. These results highlight automotive radar as a scalable and cost-effective source of supervision for building metric-aware spatiotemporal VLMs for real-world autonomous driving.
Pou-Chun Kung, Aryaman Rao, U. Sahai et al.· 0 citations
This work introduces SimBEV2X, an advanced synthetic data generation tool built on the CARLA simulator, and proposes CoBEVFusion, a novel architecture that combines CoopDet3D with fused axial attention (FAX) for context-aware multi-agent feature aggregation, resulting in superior performance.
Goodarz Mehr, Sepideh Gohari, Montasir Abbas et al.· 0 citations