Trajectory planning is crucial in multi-robot systems, particularly in environments with numerous obstacles. While extensive research has been conducted in this field, the challenge of coordinating multiple robots to flow collectively from one side of the map to the other—such as in crossing missions through obstacle-rich spaces—has received limited attention. This paper focuses on this directional traversal scenario by introducing a real-time scheduling scheme that enables multi-robot systems to move from edge to edge, emulating the smooth and efficient flow of water. Inspired by network flow optimization, our scheme decomposes the environment into a flow-based network structure, enabling the efficient allocation of robots to paths based on real-time congestion levels. The proposed scheduling planner operates on top of existing collision avoidance algorithms, aiming to minimize overall traversal time by balancing detours and waiting times. Simulation results demonstrate the effectiveness of the proposed scheme in achieving fast and coordinated traversal. Furthermore, real-world flight tests with ten drones validate its practical feasibility. This work contributes a flow-inspired, real-time scheduling planner tailored for directional multi-robot traversal in complex, obstacle-rich environments. Code: https://github.com/chengji253/FlowPlanner.

Light detection and ranging (LiDAR) sensors are crucial for autonomous vehicles to accurately perceive the surrounding environment. However, the sparsity and irregularity of large-scale LiDAR point clouds (LPCs) bring challenges for storage and transmission. Meanwhile, existing works usually adopt insufficient context and bring intolerable computation complexity, especially for high-precision LPC reconstruction. To address these problems, we propose a novel self-attention-based framework for LPC compression and reconstruction in autonomous driving environments. Specifically, our approach employs a robust backbone for octree-based feature extraction, which can be pretrained and easily extended to various tasks, thereby reducing the need for extensive task-specific architectural modifications. The backbone constructs node sequences of octree by nonoverlapping context windows and shares the result of a multihead self-attention (MSA) operation among them. Considering the similarity in features among sibling nodes, we design a locally enhanced module for exploiting sibling features and a positional encoding generator for enhancing the translation invariance of the octree node sequence. During postprocessing, we further propose an offset prediction model to reduce coordinate distortions caused by voxelization. Experimental results indicate that compared to the benchmark geometry-based point cloud compression (GPCC), our approach achieves gains of up to 54.4% for geometry and 6.8% for intensity, while compared to the attention-based baseline, we achieve up to 99% reduction in coding time. We believe that our approach effectively mines the spatial geometric features in LPCs and has low coupling for specific tasks, which will boost the related applications from algorithm optimization to industrial products.

In resource-constrained vehicle systems, establishing consistency between multi-view scenes and driver gaze remains challenging. Prior methods mainly focus on cross-source data fusion, estimating gaze or attention maps through unidirectional implicit links between scene and facial features. Although bidirectional projection can correct misalignment between predictions and ground truth, the high resolution of scene images and complex semantic extraction incur heavy computational loads. To address these issues, we propose a lightweight driver-attention estimation framework that leverages geometric consistency between scene and gaze to guide feature extraction bidirectionally, thereby strengthening representation. Specifically, we first introduce a lightweight feature extraction module that captures global and local information in parallel through dual asymmetric branches to efficiently extract facial and scene features. An information cross fusion module is then designed to promote interaction between the scene and gaze streams. The multi-branch architecture extracts gaze and geometric cues at multiple scales, reducing the computational redundancy caused by mixed features when modeling geometric consistency across both views. Experiments on a large public dataset show that incorporating scene information introduces no significant computational overhead and yields a better trade-off between accuracy and efficiency. Moreover, leveraging bidirectional projection and the temporal continuity of gaze, we preliminarily explore the framework's potential for predicting attention trends.

Gaze direction serves as a pivotal indicator for assessing the level of driver attention. While image-based gaze estimation has been extensively researched, there has been a recent shift towards capturing gaze direction from video sequences. This approach encounters notable challenges, including the comprehension of the dynamic pupil evolution across frames and the extraction of head pose information from a relatively static background. To surmount these challenges, we introduce a dual-stream deep learning framework that explicitly models the displacement changes of the pupil through a fine-grained inter-frame attention mechanism and generates weights to adjust gaze embeddings. This technique transforms the face into a set of distinct patches and employs cross-attention to ascertain the correlation between pixel displacements in various patches and adjacent frames, thereby tracking spatial dynamics within the sequence. Our method is validated using two publicly available driver gaze datasets, and the results indicate that it achieves state-of-the-art performance or is on par with the best outcomes while reducing the parameters.

Fully sparse 3D detectors have recently gained significant attention due to their efficiency in long-range detection. However, sparse 3D detectors extract features only from non-empty voxels, which impairs long-range interactions and causes the center feature missing. The former weakens the feature extraction capability, while the latter hinders network optimization. To address these challenges, we introduce the Fully Sparse Hybrid Network (FSHNet). FSHNet incorporates a proposed SlotFormer block to enhance the long-range feature extraction capability of existing sparse encoders. The SlotFormer divides sparse voxels using a slot partition approach, which, compared to traditional window partition, provides a larger receptive field. Additionally, we propose a dynamic sparse label assignment strategy to deeply optimize the network by providing more high-quality positive samples. To further enhance performance, we introduce a sparse upsampling module to refine downsampled voxels, preserving fine-grained details crucial for detecting small objects. Extensive experiments on the Waymo, nuScenes, and Argoverse2 benchmarks demonstrate the effectiveness of FSHNet. The code is available at https://github.com/Say2L/FSHNet.

High-quality LiDAR point cloud (LPC) coding is essential for efficiently transmitting and storing the vast amounts of data required for accurate 3D environmental representation. The Octree-based entropy coding framework has emerged as the predominant method, however, previous study usually overly relies on large-scale attention-based context prediction to encode Octree nodes, overlooking the inherent correlational properties of this structure. In this paper, we propose a novel Graph-driven Attention-based Entropy Model (GAEM), which adopts partitioned graph attention mechanisms to uncover contextual dependencies among neighboring nodes. Different from the Cartesian coordinate-based coding mode with higher redundancy, GAEM uses the multi-level spherical Octree to organize point clouds, improving the quality of LPC reconstruction. GAEM combines graph convolution for node feature embedding and grouped-graph attention for exploiting dependency among contexts, which preserves performance in low-computation using localized nodes. Besides, to further increase the receptive field, we design a high-resolution cross-attention module introducing sibling nodes. Experimental results show that our method achieves state-of-the-art performance on the LiDAR benchmark SemanticKITTI and MPEG-specified dataset Ford, compared to all baselines. Compared to the benchmark GPCC, our method achieves gains of up to 53.9% and 53.6% on SemanticKITTI and Ford while compared to the sibling-introduced methods, we achieve up to 42.3% and 44.7% savings in encoding/decoding time. In particular, our GAEM allows for extension to downstream tasks (i.e.,vehicle detection and semantic segmentation), further demonstrating the practicality of the method.

LiDAR point cloud (LPC) compression is an indispensable component for 3D vision tasks, especially for dynamic point clouds. However, the existing methods based on traditional spatial-temporal attention are immature, causing little improvement in inter-frame feature extraction. In this paper, we propose Diverse Attention-based Point Cloud Compression (DAPCC), an LPC compression entropy model combining aggregation embedding modules for temporal point matching and spatial-temporal attention blocks for dynamic Octree node encoding, which can effectively utilize the change information of dynamic point clouds. Specifically, we first introduce aggregation embedding to match the Octree sequences from two sweeps to establish temporal correlation. To effectively capture the feature details, we further design local and global combined attention for the spatial-temporal information of point clouds which can focus on the whole context. Finally, we organize a symmetric MLP module capable of strengthening vital features. We conduct experiments of static and dynamic compression on both indoor/outdoor point cloud benchmark datasets (i.e., ScanNet, SemanticKITTI, and MPEG Common Test Conditions (CTC) Category 3 datasets) and downstream applications (i.e., vehicle detection and semantic segmentation). Compared with the previous state-of-the-art methods, our method achieves up to 14.7% bpp and 45% decoding time savings and adapts to the downstream tasks with almost no impact on performance.

Gaze estimation can be used for assessing the attention level of drivers. Current works predominantly focus on enhancing model accuracy, often overlooking the influence of input sample and label uncertainty. In this paper, we propose a framework for uncertainty modeling in driver gaze estimation via feature disentanglement, referred to as UnMoDE. Our approach begins by extracting facial information into distinct feature spaces using an asymmetric dual-branch encoder to obtain gaze features. Subsequently, a multi-layer perceptron (MLP) is employed to project gaze features and labels into an embedding space, representing them as Gaussian distributions. The uncertainty is described using a covariance matrix. Random sampling is applied to derive samples from the gaze embedding distribution to estimate the most probable embedding representation. This estimated representation is then used to regress the gaze direction and is projected back into the gaze feature space, along with identity information, to facilitate facial reconstruction. Extensive experimental evaluations demonstrate that UnMoDE significantly outperforms baseline and state-of-the-art methods on the latest benchmark datasets collected for drivers, particularly in reducing the number of samples with significant errors.

The three-dimensional vascular model reconstructed from CT images is widely used in medical diagnosis. At different phases, the beating of the heart can cause deformation of vessels, resulting in different vascular imaging states and false positive diagnostic results. The 4D model can simulate a complete cardiac cycle. Due to the dose limitation of contrast agent injection in patients, it is valuable to synthesize a 4D coronary artery trees through finite phases imaging. In this paper, we propose a method for generating a 4D coronary artery trees, which maps the systole to the diastole through deformation field prediction, interpolates on the timeline, and the motion trajectory of points are obtained. Specifically, the centerline is used to represent vessels and to infer deformation fields using cube-based sorting and neural networks. Adjacent vessel points are aggregated and interpolated based on the deformation field of the centerline point to obtain displacement vectors of different phases. Finally, the proposed method is validated through experiments to achieve the registration of non-rigid vascular points and the generation of 4D coronary trees.

Point cloud compression is an essential technology for efficient storage and transmission of 3D data. Previous methods usually use hierarchical tree data structures for encoding the spatial sparseness of point clouds. However, the node context within the tree is not fully discovered since the feature space among nodes varies significantly. To address this problem, we innovatively represent the LiDAR points in a two-octree structure instead of using traditional single-octree coding, and then design the cross-attention model to capture the hierarchical features between different octrees, of which each octree incorporates a transformer-based deep entropy model and an arithmetic encoder. Besides, we introduce the untied cross-aware position encoding with principal component analysis and different projection matrices, which enhances the correlations over two octrees’ attention feature embeddings. Experimental results show that our method outperforms the previous state-of-the-art works, achieving up to 8.2% Bpp savings on point cloud benchmark datasets with different lasers.

With the development and application of swarm drones, some researchers have tried to replicating the migration patterns of geese in drones swarm formation to extend their lifetime. However, the problem of performing appropriate position reconfiguration based on the battery energy still remains an unsolved issue. This paper proposes an efficient position reconfiguration approach that reduces the energy consumption imbalance of the swarm and prolongs the lifetime. The approach includes: (1) a two-step MIP (mixed-integer programming)-based optimization method. (2) a two-step heuristic algorithm that can run in pseudo-polynomial time and without the need for an optimization solver. The approach provides a complete position reconfiguration solution that determines (i) the number of position reconfiguration; (ii) which drones need to exchange positions in every position reconfiguration; (iii) the length of time to maintain each position before next reconfiguration. Finally, the approach is compared with other three methods in experiments which demonstrate the effectiveness of it.

LiDAR-based 3D object detection has made impressive progress recently, yet most existing models are black-box, lacking interpretability. Previous explanation approaches primarily focus on analyzing image-based models and are not readily applicable to LiDAR-based 3D detectors. In this paper, we propose a feature factorization activation map (FFAM) to generate high-quality visual explanations for 3D detectors. FFAM employs non-negative matrix factorization to generate concept activation maps and subsequently aggregates these maps to obtain a global visual explanation. To achieve object-specific visual explanations, we refine the global visual explanation using the feature gradient of a target object. Additionally, we introduce a voxel upsampling strategy to align the scale between the activation map and input point cloud. We qualitatively and quantitatively analyze FFAM with multiple detectors on several datasets. Experimental results validate the high-quality visual explanations produced by FFAM. The code is available at \url{https://anonymous.4open.science/r/FFAM-B9AF}.

Point cloud compression with a higher compression ratio and tiny loss is essential for efficient data transportation. However, previous methods that depend on 3D convolution or frequent multi-head self-attention operations bring huge computations. To address this problem, we propose an octree-based Transformer compression method called OctFormer, which does not rely on the occupancy information of sibling nodes. Our method uses non-overlapped context windows to construct octree node sequences and share the result of a multi-head self-attention operation among a sequence of nodes. Besides, we introduce a locally-enhance module for exploiting the sibling features and a positional encoding generator for enhancing the translation invariance of the octree node sequence. Compared to the previous state-of-the-art works, our method obtains up to 17% Bpp savings compared to the voxel-context-based baseline and saves an overall 99% coding time compared to the attention-based baseline.

The 2-dimensional cost map has been widely used for the navigation of ground mobile robot. Although it is effective when the ground is flat, it becomes clumsy and ineffective when the ground has some slopes, where such slopes are often misjudged as the forbidden area by the cost map. For this reason, we propose a slope-adaptive navigation approach based on multilayer cost map in this paper. Instead of taking the point cloud of slope as obstacles, we actively construct a multi-layer cost map that takes slope information into the map in the stage of building environment map. A slope detection algorithm is developed to switch the cost map during the robot navigation. The slope is then considered as a passable road, only with extra cost. In the case that the slope leads the robot to a new floor, we adopt the Aruco code to switch the map information, such that the navigation can still keep working. Both simulation and real-world experimental results demonstrate the high effectiveness of our proposed approach.

In recent years, edge computing (EC), as an emerging technology, has been widely used in various industries. It can meet the needs of industries in real-time business, application intelligence, security and privacy protection. However, edge devices may not always be trustworthy in the edge computing environment. Moreover, traditional edge computing systems have ignored the fact that the computation capability of user device can also be used. In this paper, we propose the Minimum Computation Latency Secure Edge Computing (MCLSEC) scheme to minimize computation latency and provide the security of computing data by utilizing linear coding and the resources of both edge devices and user device. Specifically, we consider the matrix multiplication as a computation task, which is an important module in many application operations, such as machine learning, big data analysis, etc. We firstly theoretically analyze the total computation latency of edge devices and user device in the coded edge computing. We then give the design of the MCLSEC scheme, which includes of the coding scheme and the task allocation scheme. Moreover, we also give theoretical analysis to show the proposed MCLSEC scheme is secure and optimal. Finally, we conduct extensive simulation experiments to show the effectiveness of the proposed scheme. Compared with the existing schemes, MCLSEC scheme significantly reduces the computation latency of edge computing while ensuring data confidentiality.

A key challenge for autonomous driving is to process a massive amount of sensor data and make safe and reliable decisions in real time. However, autonomous vehicles often have insufficient onboard resources to provide the required computation capacity. To address this problem, this article advocates a novel approach to offload computation-intensive autonomous driving services to roadside units and cloud for swift executions. Our approach combines an integer linear programming (ILP) formulation for offline optimization of the scheduling strategy and a fast heuristics algorithm for online adaptation. We verify our technique with both synthetic task graphs and real-world deployment. The experimental results show that our approach can improve system performance effectively.

Scene flow is an essential part of a stereo-based perception system for autonomous driving and mobile robotics. As in most of these platforms, the computing resource is limited but the computing requirement is high, embedded and parallelized algorithms are of vital importance for real-time tasks. This paper develops a cross-platform embedded scene flow algorithm by using an OpenCL (Open Computing Language) programming. Meanwhile, we propose a method to achieve a good performance by using a novel coarse-grained software pipeline for the embedded stream application. Experimental results show that the proposed algorithm can boost the average processing speed to 50 fps for different commercial off-the-shelf (COTS) hardware, including desktop graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and mobile phone platforms. For certain GPUs, the peak frame rates can also reach 1000 fps. By comparing the efficiency among the serial platform, we illustrate that with the help of OpenCL programming, COTS platforms can provide enough computing resources for the stereo-based perception algorithm.

Scene flow is a key function of stereo-based environment perception system for mobile robotics and autonomous vehicle. Due to the heavy computing requirement and the limited computing resource, parallelized and embedded algorithms become quite important for the application of the mobile robotics. This paper develops a cross-platform embedded scene flow algorithm by using a coarse-grained software pipeline and OpenCL programming language. Our OpenCL algorithm is tested on 10 video streams from different datasets with different scenarios on different commercial-off-the-shelf (COTS) hardware. The average frame rates for the 10 videos can reach about 50 fps on both GPU and mobile device. The peak frame rates for certain videos on GPU can reach almost 450 fps. We also demonstrate that the COTS platform can provide sufficient computing power for stereo-based perception algorithm potentially by using OpenCL programming.

An extended-state-observer-based double-loop integral sliding-mode controller for electronic throttle (ET) is proposed by factoring the gear backlash torque and external disturbance to circumvent the parametric uncertainties and nonlinearities. The extended state observer is designed based on a nonlinear model of ET to estimate the change of throttle opening angle and total disturbance. A double-loop integral sliding-mode controller consisting of an inner loop and an outer loop is presented based on the opening angle and opening angle change errors of ET through Lyapunov stability theory. Numerical experiments are conducted using simulation. The results show that the accuracy and the response time of the proposed controller are better than those of the back-stepping and sliding mode control.

This paper focuses on the problem of adaptive tracking for a class of stochastic mechanical control systems with unknown parameters. By reasonably introducing random noise, a method to construct stochastic Lagrangian control systems is given. Under some milder assumptions, an adaptive tracking controller is designed such that the mean square of the tracking error converges to an arbitrarily small neighborhood of zero by tuning design parameters.