Unmanned Aerial Vehicle (UAV)-based geo-localization supports autonomous navigation in GPS-denied or GPS-degraded environments by matching real-time UAV-view images with geo-referenced satellite imagery. A key challenge is to learn a discriminative retrieval space that remains robust to severe cross-view viewpoint discrepancies and temporal shifts. Although visual appearance provides fine-grained discriminative cues, it is sensitive to illumination, seasonal, and perspective variations. In contrast, geographic structures, such as road layouts and building footprints, offer more stable cues, but explicit structural modeling often increases computational cost. To balance robustness and efficiency, we propose STAR, a lightweight STructure-Aware Representation learning framework. STAR uses structural semantics as training-time geometric guidance for visual retrieval manifold learning. Specifically, a structural stream derives structure-aware representations from paired UAV and satellite images and constructs pairwise structural affinities to describe geographic layout relationships. These affinities regularize the discriminative retrieval space at the manifold level, enabling compact visual descriptors to retain structural awareness for robust cross-view matching. We further introduce UniT-1652, a benchmark for evaluating UAV-based geo-localization under temporal shifts. Extensive experiments on University-1652, SUES-200, and UniT-1652 show that STAR achieves state-of-the-art performance across multiple retrieval settings, with improved robustness to temporal and viewpoint variations and fewer parameters than existing methods. 

Developing precise and computationally efficient traffic accident anticipation system is crucial for contemporary autonomous driving technologies, enabling timely intervention and loss prevention. In this study, we propose an accident anticipation framework employing a dual-branch architecture that effectively integrates visual information from dashcam videos with structured textual data derived from accident reports. Furthermore, we introduce a feature aggregation method that facilitates seamless integration of multimodal inputs through large models (GPT-4o, Long-CLIP), complemented by targeted prompt engineering strategies to produce actionable feedback and standardized accident archives. Comprehensive evaluations conducted on benchmark datasets (Dashcam Accidents Dataset (DAD), Car Crash Dataset (CCD), and AnAn Accident Detection (A3D)) validate the superior predictive accuracy, enhanced responsiveness, reduced computational overhead, and improved interpretability of our approach, thus establishing a new benchmark for state-of-the-art performance in traffic accident anticipation.

In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge. This paper introduces a sophisticated encoder-decoder framework, developed to address visual grounding in AVs. Our Context-Aware Visual Grounding (CAVG) model is an advanced system that integrates five core encoders—Text, Emotion, Image, Context, and Cross-Modal—with a multimodal decoder. This integration enables the CAVG model to adeptly capture contextual semantics and to learn human emotional features, augmented by state-of-the-art Large Language Models (LLMs) including GPT-4. The architecture of CAVG is reinforced by the implementation of multi-head cross-modal attention mechanisms and a Region-Specific Dynamic (RSD) layer for attention modulation. This architectural design enables the model to efficiently process and interpret a range of cross-modal inputs, yielding a comprehensive understanding of the correlation between verbal commands and corresponding visual scenes. Empirical evaluations on the Talk2Car dataset, a real-world benchmark, demonstrate that CAVG establishes new standards in prediction accuracy and operational efficiency. Notably, the model exhibits exceptional performance even with limited training data, ranging from 50% to 75% of the full dataset. This feature highlights its effectiveness and potential for deployment in practical AV applications. Moreover, CAVG has shown remarkable robustness and adaptability in challenging scenarios, including long-text command interpretation, low-light conditions, ambiguous command contexts, inclement weather conditions, and densely populated urban environments.