Autonomous vehicle trajectory prediction faces significant challenges in complex traffic scenarios due to sensor occlusions and limited single-viewpoint perception. Cooperative prediction among connected vehicles and infrastructure offers a promising solution. Existing approaches suffer from rigid architectural coupling, requiring homogeneous models and a fixed number of collaborators, making them impractical for dynamic real-world scenarios. Moreover, a critical yet underexplored domain shift problem arises when models trained on single-viewpoint datasets are applied to collaborative environments, leading to substantial performance degradation. To address these limitations, we propose the Bridging Infrastructure and Vehicles (BIV) cooperative prediction framework, a plug-and-play solution that enables collaboration among an arbitrary number of heterogeneous trajectory prediction systems. The BIV framework operates through three stages: individual prediction by each collaborator using their domain-specific models, cooperative sharing of predicted trajectories, and the fusion of predictions using a novel Dynamic Time Warping-based Non-Maximum Suppression (DTW-NMS) mechanism. The DTW-NMS abstracts potential future trajectories of target agents by leveraging the similarity of coordinate sequences to capture safety-critical maneuvers. The proposed framework effectively overcomes the domain shift between single-viewpoint training and collaborative deployment. As an offline collaborative approach, the BIV framework demonstrates superior performance compared to all existing offline methods, while also outperforming most online methods. The BIV framework surpasses the best offline collaborative trajectory prediction models, reducing minADE and minFDE by up to 23.15% and 25.67%, respectively, while lowering the miss rate by 25.93%.

Multi-agent trajectory prediction at signalized intersections is pivotal for the safety of autonomous driving and the efficiency of intelligent transportation systems. However, conventional vehicle-centric approaches are limited by restricted perception ranges and occlusion. Vehicle-to-Everything (V2X) cooperation is widely regarded as an effective approach to alleviating these limitations. Furthermore, existing cooperative systems often suffer from complex coupling and selection bias, hindering universal and real-time service. To address these challenges, this paper introduces a novel Infrastructure-to-Everything (I2X) collaborative prediction scheme. This scheme decouples infrastructure capabilities from vehicle requests by independently forecasting and broadcasting trajectories for all detected vehicles. Building on this scheme, we propose I2XTraj, a dedicated infrastructure-based model that leverages three core mechanisms. First, a continuous signal-informed mechanism to adaptively encode real-time traffic light information. Second, a maneuver strategy awareness mechanism that integrates intersection geometric constraints to estimate maneuver distributions. Third, a spatial-temporal-mode attention network to refine multi-agent interactions. Extensive evaluations on two real-world datasets, V2X-Seq and SinD, demonstrate the superiority of our approach. In both single-infrastructure and collaborative scenarios, I2XTraj outperforms state-of-the-art methods by over 30% and 15%, respectively, confirming its strong generalizability and robustness in complex intersection environments.

Robust object detection for Unmanned Surface Vehicles (USVs) in complex water environments is essential for reliable navigation and operation. Specifically, water surface object detection faces challenges from blurred edges and diverse object scales. Although vision-radar fusion offers a feasible solution, existing approaches suffer from cross-modal feature conflicts, which negatively affect model robustness. To address this problem, we propose a robust vision-radar fusion model WS-DETR. In particular, we first introduce a Multi-Scale Edge Information Integration (MSEII) module to enhance edge perception and a Hierarchical Feature Aggregator (HiFA) to boost multi-scale object detection in the encoder. Then, we adopt self-moving point representations for continuous convolution and residual connection to efficiently extract irregular features under the scenarios of irregular point cloud data. To further mitigate cross-modal conflicts, an Adaptive Feature Interactive Fusion (AFIF) module is introduced to integrate visual and radar features through geometric alignment and semantic fusion. Extensive experiments on the WaterScenes dataset demonstrate that WS-DETR achieves state-of-the-art (SOTA) performance, maintaining its superiority even under adverse weather and lighting conditions.

Gas recognition technology has received considerable attention from researchers in recent years. While successful in controlled environments, these algorithms often struggle in real-world industrial applications due to two key challenges: the lack of standardized performance benchmarks across datasets and over-reliance on multiple sensors. The lack of benchmarks hinders effective model comparison, while extensive sensor requirements increase hardware complexity and costs, limiting deployment flexibility. To address these gaps, we propose four specialized evaluation metrics-–coverage, identification efficiency, confusion rate, and cross identification rate—that unify performance evaluation across diverse datasets and enable a more precise, fair assessment of gas recognition algorithms. Leveraging these metrics, we introduce Wavelet Attention Gated Recurrent Unit (GRU) (WAG), a novel model that reduces the sensor requirement while preserving competitive recognition accuracy. By integrating wavelet transformation with external attention for robust frequency-domain feature extraction with GRU structures for efficient sequence modelling, WAG substantially minimizes hardware complexity and cost, paving the way for scalable, real-world gas recognition applications in resource-constrained environments.

Convolutional Neural Networks (CNNs) have exhibited great performance in discriminative feature learning for complex visual tasks. Besides discrimination power, interpretability is another important yet under-explored property for CNNs. One difficulty in the CNN interpretability is that filters and image classes are entangled. In this paper, we introduce a novel pathway to alleviate the entanglement between filters and image classes. The proposed pathway groups the filters in a late conv-layer of CNN into class-specific clusters. Clusters and classes are in a one-to-one relationship. Specifically, we use the Bernoulli sampling to generate the filter-cluster assignment matrix from a learnable filter-class correspondence matrix. To enable end-to-end optimization, we develop a novel reparameterization trick for handling the non-differentiable Bernoulli sampling. We evaluate the effectiveness of our method on ten widely used network architectures (including nine CNNs and a ViT) and five benchmark datasets. Experimental results have demonstrated that our method PICNN (the combination of standard CNNs with our proposed pathway) exhibits greater interpretability than standard CNNs while achieving higher or comparable discrimination power.

Trajectory prediction is critical for the safe planning and navigation of automated vehicles. The trajectory prediction models based on the neural networks are vulnerable to adversarial attacks. Previous attack methods have achieved high attack success rates but overlook the adaptability to realistic scenarios and the concealment of the deceits. To address this problem, we propose a speed-adaptive stealthy adversarial attack method named SA-Attack. This method searches the sensitive region of trajectory prediction models and generates the adversarial trajectories by using the vehicle-following method and incorporating information about forthcoming trajectories. Our method has the ability to adapt to different speed scenarios by reconstructing the trajectory from scratch. Fusing future trajectory trends and curvature constraints can guarantee the smoothness of adversarial trajectories, further ensuring the stealthiness of attacks. The empirical study on the datasets of nuScenes and Apolloscape demonstrates the attack performance of our proposed method. Finally, we also demonstrate the adaptability and stealthiness of SA-Attack for different speed scenarios. Our code is available at the repository: https://github.com/eclipse-bot/SA-Attack.

With the flourishing development of intelligent warehousing systems, the technology of Automated Guided Vehicle (AGV) has experienced rapid growth. Within intelligent warehousing environments, AGV is required to safely and rapidly plan an optimal path in complex and dynamic environments. Most research has studied deep reinforcement learning to address this challenge. However, in the environments with sparse extrinsic rewards, these algorithms often converge slowly, learn inefficiently or fail to reach the target. Random Network Distillation (RND), as an exploration enhancement, can effectively improve the performance of proximal policy optimization, especially enhancing the additional intrinsic rewards of the AGV agent which is in sparse reward environments. Moreover, most of the current research continues to use 2D grid mazes as experimental environments. These environments have insufficient complexity and limited action sets. To solve this limitation, we present simulation environments of AGV path planning with continuous actions and positions for AGVs, so that it can be close to realistic physical scenarios. Based on our experiments and comprehensive analysis of the proposed method, the results demonstrate that our proposed method enables AGV to more rapidly complete path planning tasks with continuous actions in our environments. A video of part of our experiments can be found at https://youtu.be/lwrY9YesGmw.

For 3D environmental perception tasks, light detection and ranging (LiDAR) assumes a crucial role by supplying extensive data in 3D space. In the context of deep learning-based LiDAR-only 3D object detectors, practical challenges like occlusion and signal missing lead to the loss of partial shape information, thereby causing a decline in detection accuracy. To tackle this issue, we propose a two-stage LiDAR 3D object detector that includes the cuboid-wise shape augmentation network to supplement instance-level foreground points with geometric structure information. Dense point clouds are achieved by introducing gridding procedure and 3D encoder-decoder. Besides, we further capture contextual information and reweight grid points within each proposal. The original region of interest (RoI) features are aggregated with the augmented features in refinement stage to recover intact shape details. The performance of our proposed method on KITTI dataset demonstrates its effectiveness in 3D object detection.

Several research works have been proposed to evaluate the robustness of deep-learning-based semantic segmentation models for automated driving under adversarial attacks. However, the types of tested adversarial examples and evaluated segmentation models are limited in the previous empirical studies, which imposes the restrictions on the understanding of the robustness of semantic segmentation models. To alleviate these problems, we would promote the research on the robustness of semantic segmentation models systematically from two aspects of influence factors: internal factors of model structures and external factors of environmental perturbations at the data level. In this paper, we provide a comprehensive study using these typical models with different internal structures: Fully Convolutional Networks (FCN), Pyramid Scene Parsing Network (PSPNet), DeepLabV3+, and SegNet with different backbones. These models would be evaluated on the metrics of robustness under both white-box attacks and black-box attacks. Based on our experiment, we make qualitative and quantitative analyses of the robustness of diverse models under different influence factors. With more empirical study cases, our work gives inspiration to the robustness study of semantic segmentation for automated driving which is meaningful and beneficial to the safety of the intended functionality (SOTIF).