High-definition (HD) maps play a crucial role in autonomous driving by providing a reliable foundation for behavior prediction and path planning. Recent methods model map elements as point sets and employ Transformer-based detection frameworks for end-to-end vectorized map construction. However, these approaches do not fully exploit geometric structural properties, thereby limiting the accuracy of predicting the shape and position of map elements. To address this limitation, we propose a novel method calledGraphMapTR, which models map elements as graph structures. Each map element is represented as a subgraph where key points serve as nodes and edges capture structural continuity. To optimize the node query embeddings within subgraphs, we design a Graph Transformer-based decoder comprising three core components:Multi-head Self-Attention Between Subgraphs (MHSA-BSG), which facilitates efficient feature interaction across different subgraphs while enhancing node attention to their respective subgraph regions;Local Geometric-aware Self-Attention Within Subgraphs (LGSA-WSG), which dynamically aggregates global node relationships and local edge structures to enrich node representations; andDeformable Cross-Attention (DCA-BEV), which refines node embeddings through interaction with BEV features. In LGSA-WSG, priors from the rasterized map segmentation branch are incorporated to generate local geometric consistency scores (LGC-scores), which constitute edge structural bias. Extensive experiments demonstrate thatGraphMapTRsignificantly outperforms state-of-the-art methods. In particular, it outperforms the state-of-the-art algorithm MapTRv2 by 5.0% mAP and 3.4% mAP on the nuScenes and Argoverse2 datasets, respectively.

The online vectorized map construction technique employs a neural network model to forecast the vectorized representation of a specific region around automobiles, using data obtained from sensors mounted on automobiles. Due to advances in end-to-end object detection with transformers framework, the research on query-based online mapping has attracted substantial attention. However, the fixed number of queries and the random initialization of query embeddings constrain the model's performance. Moreover, the transformer architecture for object detection is based on the assumption that queries are identically distributed and independent, a premise that is not entirely applicable to map point queries which possess established subordinate relationships with map element instances. To address these issues, we initially incorporate a simplified transformer layer that utilizes semantic priors in bird's-eye view features for query initialization. The queries are then sent to a transformer-based map decoder for optimization and combined with a dynamic query management mechanism to eliminate low-confidence queries, hence maintaining computational efficiency. Furthermore, to guarantee that point queries within each instance preserve a consistent representation and avoid feature confusion among map element instances, we proposed an instance internal consistency map decoder. We conduct extensive experiments on commonly used map construction datasets to evaluate the proposed method. The experimental results demonstrate that our proposed method achieves state-of-the-art performance on the nuScenes and Argoverse 2 datasets.

Learning local features is a fundamental task for many computer vision applications. Existing methods often struggle to maintain robustness and accuracy in extracting local features, especially in complex environments with numerous interfering objects. Although some studies have integrated semantic information into local feature extraction networks to enhance discrimination, their effectiveness remains limited. Therefore, this paper fully considers the importance of semantic information for feature extraction and proposes a semantically enhanced local feature extraction network framework. This framework includes a local feature network, a semantic segmentation network, and a reinforcement learning framework. Semantic information is incorporated into feature heatmaps and feature descriptors to improve the accuracy of feature points. Subsequently, the local feature network is continuously optimized by a reinforcement learning algorithm based on semantic information and matching ground truth to enhance robustness, ensuring that the final local features achieve optimal performance. Extensive experiments on three publicly available datasets validate the effectiveness of the proposed local feature network.

Learning-based local feature extraction algorithms have advanced considerably in terms of robustness. While excelling at enhancing feature robustness, some outstanding algorithms tend to neglect discriminability—a crucial aspect in vision tasks. With the increase of deep learning convolutional layers, we observe an amplification of semantic information within images, accompanied by a diminishing presence of spatial structural information. This imbalance primarily contributes to the subpar feature discriminability. Therefore, this paper introduces a novel network framework aimed at imbuing feature descriptors with robustness and discriminative power by reinforcing spatial structural information. Our approach incorporates a spatial structure enhancement module into the network architecture, spanning from shallow to deep layers, ensuring the retention of rich structural information in deeper layers, thereby enhancing discriminability. Finally, we evaluate our method, demonstrating superior performance in visual localization and feature-matching tasks.

In this paper, we introduce a novel negative-capacitance device, named as Negative-Capacitance Independent Multi-Gate FinFET (NC-IMG-FinFET), by stacking a thin ferroelectric (FE) layer into the front gate stack of a baseline traditional independent Multi-Gate FinFET (IMG-FinFET). The capacitance model of the proposed NC-IMG-FinFET is addressed, and then its characteristics and performance are simulated by HSPICE. The simulation results demonstrated that the NC-IMG-FinFET exhibits more excellent electrical characteristics compared with that of the traditional IMG-FinFET, in terms of smaller Sub-threshold Swing (SS) (42mV/dec at room temperature), smaller off-state current, bigger on-state current, and better electrostatic control of the back-gate electrode for the threshold voltage. It is expected that this work may provide some help to the low power applications of integrated circuits.