End-to-end models are emerging as the mainstream in autonomous driving perception. However, the inability to meticulously deconstruct their internal mechanisms results in diminished development efficacy and impedes the establishment of trust. Pioneering in the issue, we present the Independent Functional Module Evaluation for Bird's-EyeView Perception Model (BEV-IFME), a novel framework that juxtaposes the module's feature maps against Ground Truth within a unified semantic Representation Space to quantify their similarity, thereby assessing the training maturity of individual functional modules. The core of the framework lies in the process of feature map encoding and representation aligning, facilitated by our proposed two-stage Alignment AutoEncoder, which ensures the preservation of salient information and the consistency of feature structure. The metric for evaluating the training maturity of functional modules, Similarity Score, demonstrates a robust positive correlation with BEV metrics, with an average correlation coefficient of 0.9387, attesting to the framework's reliability for assessment purposes.

The fusion of RGB and depth information holds significant potential for accurate road negative obstacle identification. However, effectively leveraging these multimodal data for distinguishing fine-grained road surface defects, such as potholes and cracks, remains a challenge. Inspired by the recent progress of multimodal fusion in a variety of computer vision tasks, this paper aims to propose a novel Vision Mamba based Road Negative Obstacle Segmentation framework (RNOSMamba) that leverages the complementary strengths of optical and depth images. Toward this end, optical and depth images in the feature domain are appropriately fused to boost the performance of road negative obstacle segmentation (RNOS). The hierarchical decoder incorporates Cross-Modality State Space (CMSS) blocks and Cross-Scale Feature Fusion (CSFF) modules to refine features and produce precise segmentation masks. Extensive experiments demonstrate that the proposed RNOMamba was able to achieve 68.5% mIoU and 80.7% mF1, highlighting its potential to significantly boost the accuracy of road negative obstacle segmentation.

Bird’s-eye-view (BEV) semantic segmentation is becoming crucial in autonomous driving systems. It realizes ego-vehicle surrounding environment perception by projecting 2D multi-view images into 3D world space. Recently, BEV segmentation has made notable progress, attributed to better view transformation modules, larger image encoders, or more temporal information. However, there are still two issues: 1) a lack of effective understanding and enhancement of BEV space features, particularly in accurately capturing long-distance environmental features and 2) recognizing fine details of target objects. To address these issues, we propose OE-BevSeg, an end-to-end multimodal framework that enhances BEV segmentation performance through global environment-aware perception and local target object enhancement. OE-BevSeg employs an environment-aware BEV compressor. Based on prior knowledge about the main composition of the BEV surrounding environment varying with the increase of distance intervals, long-sequence global modeling is utilized to improve the model’s understanding and perception of the environment. From the perspective of enriching target object information in segmentation results, we introduce the center-informed object enhancement module, using centerness information to supervise and guide the segmentation head, thereby enhancing segmentation performance from a local enhancement perspective. Additionally, we designed a multimodal fusion branch that integrates multi-view RGB image features with radar/LiDAR features, achieving significant performance improvements. Extensive experiments show that, whether in camera-only or multimodal fusion BEV segmentation tasks, our approach achieves state-of-the-art results by a large margin on the nuScenes dataset for vehicle segmentation, demonstrating superior applicability in the field of autonomous driving. Our code will be released at https://github.com/SunJ1025/OE-BevSeghttps://github.com/SunJ1025/OE-BevSeg.

Modern networks require frequent configuration updates due to dynamic events, like network expansion and evolving traffic patterns. Existing network reconfiguration tools are effective in certain scenarios, but their practical deployment still has several limitations: (i) They are restricted to specific network topologies, protocols and specifications; (ii) They can cause transient violations; (iii) Their practical deployment is limited by huge computational overheads and the specialized hardware support. To address these limitations, this paper presents a Multi-Objective Optimizing Network Reconfiguration (MONR) framework, which comprises a translator and an optimizer, to automatically generate reconfiguration command sequences. The translator represents various input types into a unified graph format based on Datalog-like facts, which are regardless of the input format. Therefore, MONR supports diverse routing protocols and network specifications. The optimizer employs deep reinforcement learning techniques to simultaneously maximize the specification satisfaction of intermediate configurations and minimize both traffic shifts and the number of command updates to reduce computational overheads. It models the reconfiguration task as a multi-objective Markov Decision Process (MOMDP) and introduces a Dueling Prioritized Experience Replay Double Deep Q-Network (DPER-DDQN) algorithm to balance multiple objectives. We compare MONR with Snowcap, AED and ConfigReco. The evaluation demonstrates that MONR is 2x, 9x, and 56x faster than Snowcap, AED and ConfigReco. Furthermore, MONR maintains 100% specification consistency while reducing the traffic shifts (< 0.1) and the number of update commands (0.8 of Snowcap's).

Modern networks are susceptible to configuration errors, such as misconfigurations and policy conflicts due to the complex interactions of diverse devices through various protocols. Control plane verification offers an effective solution to prevent these errors. However, existing tools face several challenges: (i) prolonged verification times, (ii) the verification of only specific policies, and (iii) poor robustness against node and link failures. To address these issues, we propose a control plane verification framework based on a multimodal multitask learning model. This framework enables simultaneous verification of multiple policies directly from various network configuration files. The learning model utilizes modality fusion techniques to capture both topology-related and traffic-related network features. It is trained on datasets augmented with the failure model to enhance robustness against failures. We compare our framework with three state-of-the-art verification tools: Minesweeper, Hoyan, and Tiramisu. Our evaluation shows that our framework is 2600 times faster than Minesweeper, twice as fast as Hoyan, and 19 times faster than Tiramisu, while maintaining 100% verification accuracy. Furthermore, our framework excels in verifying traffic-related network policies and remains effective even under node and link failures.

Inspired by the recent progress of multimodal fusion in a variety of computer vision tasks, this letter aims to propose a two-stream fusion crater detection network (TFCDNet). Toward this end, near-infrared (IR) images and digital elevation maps (DEMs) in the feature domain are appropriately fused to boost the performance of crater detection (CD). The proposed TFCDNet includes a powerful feature-coding module that can effectively extract and fuse multimodal features. The comprehensively conducted experiments on both optical-DEM paired lunar crater detection dataset (ODPLCD) and Mars day CD (MDCD) datasets reveal that the proposed TFCDNet is capable of being more competitive than the state of the arts. As a result, this work is anticipated to spark some new thinking in CD. Relevant data in this letter can be downloaded from the websitehttps://doi.org/10.57760/sciencedb.o00009.00312.

Identifying the surface topographic changes accurately plays a vital role in the task of planetary exploration. In this study, a lightweight mobile vision transformer-based planetary image change detection (MViT-PCD) was proposed for monitoring dynamic surface changes using bitemporal images. The mobile vision transformer (MobileViT) was first introduced to make the most of the spatial information available. Subsequently, a multiscale feature differentiation and fusion (MFDF) block was adopted to improve the distinguishability of multilevel contextual information. Moreover, the strategy of information maximization (IM) was integrated to refine the model performance on the heterogeneous dataset. Then, experiments were conducted on the public Martian datasets. Compared with other state-of-the-art (SOTA) methods, the MViT-PCD provides favorable performance, with the highest accuracy of 97.2% and 82.9%, respectively, under the speed of 43.4 frame per second (FPS). Code is available athttps://github.com/lynn1023-max/MViTPCD.