Vision-and-Language Navigation (VLN) plays a critical role in tasks of embodied AI, particularly in unseen environments following natural language instructions. Recent advancements leverage large language models (LLMs) to improve the accuracy and generalizability of VLN systems by encoding image sequences as dense token representations. However, this tokenization approach incurs substantial computational overhead due to two key inefficiencies: 1) ego-centric camera views often include navigation-irrelevant re- gions (e.g., sky or distant backgrounds), and 2) high-frame-rate image sequences introduce temporal redundancy. To address these challenges, we propose Spatial-Temporal Efficient Visual Token Pruning (STEP-Nav), a unified frame- work that simultaneously prunes redundant visual tokens and fine-tunes VLN models to preserve navigation performance. In particular, STEP-Nav incorporates a distance- and content-aware token evaluation mechanism to remove irrelevant tokens at the spatial level, along with temporal level similarity-based filtering to reduce redundancy across sequential frames. To ensure pruning does not harm task performance, we introduce a distortion-aware fine-tuning strategy that aligns pruned-token representations with their full-token counterparts while maintaining navigation accuracy. Experiments on the R2R and RxR benchmarks using Navid-CE and NavGPT-2 as base models demonstrate that STEP-Nav preserves over 95% of the performance while reducing 66.7% of tokens, outperforming existing token pruning baselines.

This paper proposes a multi-vehicle cooperative control framework to address cyber-attack interference and environmental uncertainty in complex traffic scenarios. First, to tackle the cooperative adaptive cruise control (CACC) problem under network attacks, an adaptive stochastic hybrid model predictive control (SHMPC) approach is developed by integrating deep deterministic policy gradient (DDPG) with mixed logical dynamics (MLD), enabling effective mode switching and online tuning of control parameters, thereby enhancing system robustness and adaptability. Second, for safe lane changing in dynamic traffic, an stochastic model predictive control (SMPC) controller incorporating a probabilistic control barrier function (PCBF) and iterative linear quadratic regulator (iLQR)-based trajectory planning is constructed to explicitly model environmental uncertainties and enforce safety through probabilistic constraints. Finally, comprehensive simulations validate the effectiveness of the proposed models: under stochastic cyber-attacks, the tracking distance error is reduced by about 11%; for lane change tasks, the success rate is increased by 24.6%, with significantly fewer collisions. The results demonstrate the proposed framework’s superiority in robustness, safety, and response efficiency.

Vehicle trajectory prediction is important for automated vehicles to understand driving scenarios. This paper proposes an encoder-decoder network-based parameterized transfer learning (EDN-PTL) model to predict vehicle trajectory. To improve trajectory prediction accuracy, the motion interaction between the target vehicle and the surrounding vehicles is considered, and a multidimensional spatiotemporal input expansion (MSIA) strategy is proposed to extend the feature dimensions. Additionally, global and local scale features, as well as long and short horizon features, are extracted and used for interactive vehicle trajectory prediction by a CNN and LSTM-based encoder-decoder network (CNN-LSTM-EDN). Moreover, the features extracted by CNN-LSTM-EDN are integrated using a stacked convolutional social pooling network (SCSPN). To enhance the environmental adaptability of the trajectory prediction model, a PTL strategy is proposed to enable transfer learning capabilities of EDN-PTL. Based on the PTL strategy, trajectory prediction accuracy is maintained even when applied to untrained environments. The proposed EDN-PTL model is validated on three types of publicly available naturalistic datasets and compared with several baselines and state-of-the-art (SOTA) methods. The validation results demonstrate that the proposed EDN-PTL achieves better prediction accuracy, robustness, and environmental adaptability compared to the baselines and SOTA methods.

Energy optimization constitutes a paramount design consideration in the realm of embedded systems development since these devices are inherently constrained by finite battery resources. Designing and developing an effective energy-aware scheduling approach is a desirable work to provide excellent processing capability while keeping the energy consumption under control. Although previous approaches can obtain reasonable scheduling solutions for tasks with energy consumption constraints, they are computationally expensive and have deficiencies in effectiveness or efficiency due to unfair or inefficient energy pre-assignment strategies. In this article, we study the energy-aware workflow scheduling problem and present a three-stage list-based approach to minimize the schedule length of workflows in heterogeneous distributed embedded systems. First, the workflow applications and energy consumption of processors are modelled, and the energy-aware workflow scheduling problem is formulated as a non-linear mixed integer programming one with various dependency and energy constraints. Then, with an effective task prioritization strategy and a reasonable energy pre-assignment strategy, a three-stage list-based scheduling approach is proposed to schedule the tasks and minimize the schedule length of workflows. Experiments on randomly-generated and real-life workflows demonstrate that our proposed approach constantly outperforms the existing approaches and our algorithm can, respectively, reduce the normalized schedule length and the deviation ratio by 16.7% and 7.6% in average.

Search time is an important metric for regional coverage searches conducted by unmanned clusters. This paper proposes an improved proximal policy optimization (IPPO) algorithm to decrease search time while ensuring the coverage rate for a heterogeneous cluster consisting of unmanned ground vehicles and unmanned aerial vehicles (UGV-UAV). The models of the UGV-UAV cluster, search scenario, and search constraints are first developed. Then, the IPPO algorithm is designed to simultaneously learn the cross-domain actions of UGVs and UAVs. The main advantage of the IPPO is that it can achieve the cross-domain cooperative learning (CDCL) mechanism, thus ensuring the collaboration consistency of the UGV-UAV cluster and enhancing search efficiency. To analyze the IPPO-based regional coverage search performance, three state-of-the-art (SOTA) methods are selected for comparison. The validation results demonstrate that the proposed method outperforms these SOTA methods in terms of both search time and coverage rate.

Collaborative perception effectively mitigates issues such as limited field of view and occlusion by enabling multiple agents to share perceptual information. Despite its advantages, challenges persist in complex environments due to factors such as limited communication bandwidth and noisy poses, which may potentially degrade system performance. Meanwhile, a substantial amount of simulation data is widely adopted in collaborative perception to achieve high precision and real-time detection. However, the domain gap between simulated and real-world environments may result in weakened collaborative performance and hindered generalization ability. In this work, we focus on the multi-agent collaborative perception problem and propose a quantized communication and transferable fusion framework, namedQCTF, to efficiently minimize the bandwidth overhead and enhance real-world perception by leveraging unlabeled data for improved adaptability. First, we present a quantized communication method that employs multi-scale residual indices and an optimized codebook to extract robust representations while minimizing bandwidth usage. Then, we design a channel-aware selection strategy that adjusts the bandwidth volume and compensates for the quantized representation by combining the prioritized critical features with the channel dimension. Finally, we adopt a transferable fusion module to effectively bridge the simulation-to-reality domain gaps and improve perceptual capability through multi-scale adaptation discriminators. Experiments on both simulated and real-world datasets are conducted to evaluate the effectiveness of the proposed framework, and the results demonstrate that our approach consistently outperforms the existing methods in limited communication bandwidth and domain adaptation scenarios.

Due to their high flexibility and strong maneuverability, unmanned aerial vehicles (UAVs) have attracted lots of attention and are widely employed in many fields. Especially in target encirclement applications, UAVs have shown great advantages in adaptability and reliability, and can efficiently fly to and evenly surround the targets in complex and dynamic environments. In this paper, we concentrate on the cooperative target encirclement problem of heterogeneous UAVs and try to propose a multi-agent reinforcement learning approach to solve the problem. First, with the models of heterogeneous UAVs and obstacles, we analyze the collision avoidance, motion continuity, and energy consumption constraints of UAVs, and formulate the cooperative target encirclement problem as a multi-constraint combinatorial optimization one. Then, inspired by the humans’ learning experience that curiosity provides a powerful motivator for humans to explore, discover, and acquire new knowledge, we propose an extrinsic-and-intrinsic reward-based multi-agent reinforcement learning approach to cooperatively control the behaviors of UAVs and achieve the target encirclement missions. Simulation experiments with randomly generated environments are conducted to evaluate the performance of our approach, and the results show that our approach has a significant advantage in terms of average reward, encirclement success rate, encirclement time, and encirclement energy consumption.

Human-machine cooperation can simultaneously leverage the strengths of both human drivers and machines, making it a promising solution for improving driving safety, comfort, and experience. This paper designs a heterogeneous synchronous reinforcement learning (HSRL)-based human-machine shared steering decision-making (HMSSDM) strategy for intelligent vehicles. First, the vehicle dynamics, which incorporate steering characteristics, are built to quantify human driver’s steering behavior. Additionally, the scenario-oriented driving constraints (SODCs) are established to demonstrate driving constraints from traffic participants, roadside obstacles, and traffic signs. Second, to enhance the rationality and reliability of steering behaviors, the human driver’s steering behavior is evaluated using a fuzzy logic strategy, and HSRL is proposed to simultaneously determine steering actions and allocate driving authority between the human driver and machine. The main advantage of HSRL is its ability to perform both continuous domain learning (CDL) and discrete domain learning (DDL) simultaneously. Finally, the proposed method is validated using a human and hardware-in-the-loop (HHiL) experimental platform. The comparison results demonstrate that the proposed method outperforms the comparison methods in terms of driving safety, comfort and experience.

Lane recognition is an important component of autonomous driving system and advanced driving assistance system (ADAS) for intelligent vehicles. In complex driving conditions, accurate and fast lane recognition is a challenging issue. In this paper, a vision-based geometric model (VBGM) is proposed for accurate and fast lane recognition in complex conditions. The framework of the VBGM includes an image preprocessing stage and a lane recognition stage. In the image preprocessing stage, the region of interest (ROI) is extracted from the original image, and the original image is transformed into an undistorted greyscale image. In the lane recognition stage, the lane contour is first extracted using the Roberts operator. Then, to accurately and quickly recognize the lane marking, a lane recognition coordinate system (LRCS) and a rotational LRCS (R-LRCS) are constructed. The distracting contours in abnormal regions are padded based on the LRCS using a contextual frames correlation (CFC) strategy, and the midpoints of the lane contour are identified based on the R-LRCS. Finally, an adaptive-order polynomial fitting model is built to fit the lane marking according to the midpoints in the LRCS. To evaluate the effectiveness of the proposed method, two state-of-the-art methods are selected for comparison. The comparative results indicate that the proposed method possesses a higher recognition rate and speed for lane recognition in complex conditions.

Heterogeneous multiprocessor architecture is frequently employed as an economical and efficient means of providing excellent parallel processing capabilities while keeping production cost and power consumption under control. Although this architecture achieves significant performance enhancement and cost reduction, it results in a serious task allocation and scheduling problem, especially for periodic tasks with data dependencies, all of which should be reasonably scheduled and executed in a timely manner such that their deadlines and dependence requirements could be satisfied even if the worst happens. In this article, we concentrate on the non-preemptive scheduling problem of periodic tasks with data dependencies upon heterogeneous multiprocessor platforms. First, with models of data-dependent tasks and heterogeneous processors, we analyze the time, space, precedence, and data dependence constraints of tasks and design an exact formulation based on the mixed integer linear programming to completely explore the solution space and produce the optimal solutions. Then, by constructing a directed acyclic graph to depict the dependence relationship of jobs generated by tasks, we propose an efficient off-line list-based scheduling algorithm to provide a reasonable time and processor allocation for each job, with a view to minimizing the completion time of jobs. Experiments with randomly generated tasks are performed to evaluate the effectiveness and efficiency of the proposed algorithm, and the experimental results show that our algorithm can averagely enhance the scheduling success ratio by 28.5%, and, respectively, reduce the task completion time and the deviation ratio by 23.3% and 17.2%, on average.

Camera-LiDAR fusion models significantly enhance perception performance in autonomous driving. The fusion mechanism leverages the strengths of each modality while minimizing their weaknesses. Moreover, in practice, camera-LiDAR fusion models utilize pre-trained backbones for efficient training. However, we argue that directly loading single-modal pre-trained camera and LiDAR backbones into camera-LiDAR fusion models introduces similar feature redundancy across modalities due to the nature of the fusion mechanism. Unfortunately, existing pruning methods are developed explicitly for single-modal models, and thus, they struggle to effectively identify these specific redundant parameters in camera-LiDAR fusion models. In this paper, to address the issue above on camera-LiDAR fusion models, we propose a novelty pruning framework Alternative Modality Masking Pruning (AlterMOMA), which employs alternative masking on each modality and identifies the redundant parameters. Specifically, when one modality parameters are masked (deactivated), the absence of features from the masked backbone compels the model to reactivate previous redundant features of the other modality backbone. Therefore, these redundant features and relevant redundant parameters can be identified via the reactivation process. The redundant parameters can be pruned by our proposed importance score evaluation function, Alternative Evaluation (AlterEva), which is based on the observation of the loss changes when certain modality parameters are activated and deactivated. Extensive experiments on the nuScene and KITTI datasets encompassing diverse tasks, baseline models, and pruning algorithms showcase that AlterMOMA outperforms existing pruning methods, attaining state-of-the-art performance.

Collaborative 3D object detection by sharing features among agents significantly enhances performance compared to single-agent detection. However, directly sharing full-sized features introduces a large communication bandwidth load. To address this challenge, existing collaborative methods adopt a request-response framework, where the ego agent sends a request, and collaborative agents respond with only the necessary parts of the features after analyzing the request. However, the frequent communication in this request-response cycle impacts real-time system performance in real-world environments by increasing overall processing time and raising the risk of message loss and communication delays. To address this challenge and enable real-time system implementation, we propose a request-free collaborative 3D object detection framework that eliminates the request-response cycle through a novel request-free response generator, named Position and Occlusion Response Generator (PORG). PORG consists of two specialized components, Position-aware Mask Generator (PaMG) and Occlusion-aware Feature Mask Generator (OaMG), which use attention mechanisms to generate the necessary response features without the request from the ego agent. To evaluate the efficiency of our proposed PORG, we conducted evaluations on both public datasets and real-world settings. We provide system implementation for both the request-response and request-free frameworks on Jetson Orin Series embedded devices, and extensive evaluation shows that PORG outperforms the baselines, achieving higher Average Precision (AP) with lower communication bandwidth in public datasets and superior real-time performance on embedded devices.

Converting natural language text into executable SQL queries significantly impacts the healthcare domain, specifically when applied to electronic medical records. Given that electronic medical records store extensive patient information in a relational multitable database, developing a Text-to-SQL parser would enable the correlation of intricate medical terminology through semantic parsing. A major challenge is designing a versatile Text2SQL parser applicable to new databases. A critical step towards this goal involves schema linking - accurately identifying references to previously unseen columns or tables during SQL creation. In response to these key challenges, we propose a novel framework—Linking Information Enhanced Text2SQL Parsing on Complex Electronic Medical Records (LI-EMRSQL). This model leverages the Poincaré distance metric detection procedure, utilizing induced relations to enhance the performance of pre-existing graph-based parsers and improve schema linkage. To enhance the generalizability of LI-EMRSQL, the detection process is completely unsupervised and does not necessitate additional parameters. On two conventional Text2SQL datasets and two EMRs Text2SQL datasets, the system delivers SOTA performance. Furthermore, notable enhancements in the model's comprehension and alignment of schemas are observed.

The regulation time is an important index for formation switching control of connected and automated vehicle (CA V) platoon. This paper proposes a time-aware formation control (T AFC) strategy to improve the formation switching performance of CA V platoon. To construct an effective information sharing mechanism among the vehicles in the platoon, a unidirectional weighted graph is designed to construct the relation of the CA V platoon and calculate the impact factor between two different vehicles. Based on the unidirectional weighted graph, the time-aware requirement is converted to the regulation order problem, and the regulation order which corresponding to the minimum time is designed. According to the T AFC, the qualitative regulation strategy of the CA V platoon and the quantitative tune-up strategy of the vehicles are determined. In order to analyze the performance of the TAFC strategy, two state-of-art methods are selected as the benchmarked methods. The validation results demonstrate the proposed method possesses better performance for formation switching control compared with the benchmarked methods.

BACKGROUND: Enhancers play a crucial role in gene regulation, and some active enhancers produce noncoding RNAs known as enhancer RNAs (eRNAs) bi-directionally. The most commonly used method for detecting eRNAs is CAGE-seq, but the instability of eRNAs in vivo leads to data noise in sequencing results. Unfortunately, there is currently a lack of research focused on the noise inherent in CAGE-seq data, and few approaches have been developed for predicting eRNAs. Bridging this gap and developing widely applicable eRNA prediction models is of utmost importance. RESULTS: In this study, we proposed a method to reduce false positives in the identification of eRNAs by adjusting the statistical distribution of expression levels. We also developed eRNA prediction models using joint gene expressions, DNA methylation, and histone modification. These models achieved impressive performance with an AUC value of approximately 0.95 for intra-cell prediction and 0.9 for cross-cell prediction. CONCLUSIONS: Our method effectively attenuates the noise generated by stochastic RNA production, resulting in more accurate detection of eRNAs. Furthermore, our eRNA prediction model exhibited significant accuracy in both intra-cell and cross-cell validation, highlighting its robustness and potential application in various cellular contexts.

The energy utilization efficiency of autonomous electric vehicles is seriously affected by the longitudinal motion control performance. However, the longitudinal motion control is constrained by the driving scene. This article proposes an energy-saving optimization and control (ESOC) method to improve the energy utilization efficiency of autonomous electric vehicles. In ESOC, the constraints from the driving scene are thoroughly considered, and the autonomous driving scene constraints are mapped to the vehicle dynamics and control domain. On this basis, the efficiency self-searching method and the multiconstraint energy-saving control strategy are designed. The main ideology of the proposed ESOC is that the energy utilization efficiency of an autonomous electric vehicle can be improved by optimizing and controlling the operation point distribution of the powertrain efficiency. The experimental results demonstrate that the operation point distribution of the autonomous electric vehicle's powertrain efficiency can be well optimized by the proposed ESOC, and the energy consumption results indicate that the proposed ESOC outperforms the state-of-the-art methods.

Unmanned aerial vehicles (UAVs) have been widely applied in civilian and military applications due to their high autonomy and strong adaptability. Although UAVs can achieve effective cost reduction and flexibility enhancement in the development of large-scale systems, they result in a serious path planning and task allocation problem. Coverage path planning, which tries to seek flight paths to cover all of regions of interest, is one of the key technologies in achieving autonomous driving of UAVs and difficult to obtain optimal solutions because of its NP-Hard computational complexity. In this paper, we study the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions. First, with models of separated regions and heterogeneous UAVs, we propose an exact formulation based on mixed integer linear programming to fully search the solution space and produce optimal flight paths for autonomous UAVs. Then, inspired from density-based clustering methods, we design an original clustering-based algorithm to classify regions into clusters and obtain approximate optimal point-to-point paths for UAVs such that coverage tasks would be carried out correctly and efficiently. Experiments with randomly generated regions are conducted to demonstrate the efficiency and effectiveness of the proposed approach.

Due to the high maneuverability and strong adaptability, autonomous unmanned aerial vehicles (UAVs) are of high interest to many civilian and military organizations around the world. Automatic path planning which autonomously finds a good enough path that covers the whole area of interest, is an essential aspect of UAV autonomy. In this study, we focus on the automatic path planning of heterogeneous UAVs with different flight and scan capabilities, and try to present an efficient algorithm to produce appropriate paths for UAVs. First, models of heterogeneous UAVs are built, and the automatic path planning is abstracted as a multi-constraint optimization problem and solved by a linear programming formulation. Then, inspired by the density-based clustering analysis and symbiotic interaction behaviours of organisms, an adaptive clustering-based algorithm with a symbiotic organisms search-based optimization strategy is proposed to efficiently settle the path planning problem and generate feasible paths for heterogeneous UAVs with a view to minimizing the time consumption of the search tasks. Experiments on randomly generated regions are conducted to evaluate the performance of the proposed approach in terms of task completion time, execution time and deviation ratio.

Vehicle mass and road grade are important to traction control and safety control of electric mining haul trucks (EMHTs), especially to autonomous EMHTs. This paper proposes a base-vector based cross iteration estimator (BVCIE) to simultaneously estimate the vehicle mass and the road grade for EMHTs. To develop the estimator, the dynamics model for EMHTs is first built and the unknown parameters are identified by the recursive least square. Then the estimator is proposed by selecting a base vector from the parameters to be estimated, and by using a cross iteration strategy to update older information. This estimator is based on the ideology that the total mass of EMHT is a constant in every travelling trip. The estimator is finally validated on different loads under different road conditions with 930E. The experimental results show that both the vehicle mass and the road grade can be estimated with good accuracy.