In the context of the Industrial Internet of Things (IIoT), whole-body mobile manipulation plays a critical role in smart manufacturing and flexible automation, which requires high-quality teleoperation data and reliable long-horizon, multimodal action prediction. We present an exoskeleton-VR teleoperation system with simple force feedback for single-operator whole-body mobile control, enabling consistent multimodal demonstrations. We further introduce MiMoCo, an encoder–decoder imitation learning framework with Efficient Context Modeling Network (ECM-Net) for linear-complexity phase-level temporal modeling and Multi-Receptive Field Fusion Network (MRF-Net) for dual-path multi-receptive-field visual-motor fusion. Real-world experiments on a mobile robot demonstrate that MiMoCo achieves competitive performance compared with state-of-the-art baselines across multiple whole-body mobile manipulation tasks, and excels in long-horizon, fine-grained subtasks. Source code is publicly available at https://github.com/meijie-jesse/MiMoCo.

Imitation learning has emerged as a promising paradigm for automating robotic fine manipulation, yet maintaining temporal coherence and execution precision in long-horizon tasks remains a formidable challenge. This requires addressing exponential error accumulation in long-horizon tasks, as well as the inherent trade-off between millimeter-level positioning precision and motion smoothness, which is critical for the stability and accuracy of robotic automation systems. To this end, we propose FreMotion, a task-driven asymmetric encoder-decoder framework designed to enhance the robustness and efficiency of robotic manipulation. Our approach introduces the Temporal Dynamic Encoder (TDE-Net), which employs linear attention with dynamic gating to capture long-range dependencies and mitigate error accumulation in continuous control. Simultaneously, the Frequency-Aware Decoder (FAD-Net) leverages a dual-path attention mechanism to explicitly disentangle and fuse high- and low-frequency information for precise visuo-motor coordination. Extensive experiments on bimanual robotic tasks demonstrate that FreMotion, with orders of magnitude fewer parameters (approx. 100M vs. Billions), not only outperforms state-of-the-art Vision-Language-Action (VLA) methods in success rate but also achieves faster inference time, thus ensuring high-frequency continuous control required for industrial automation.

Predicting potential collision events is beneficial to ensure the driving safety of autonomous vehicles. Existing graph-based collision prediction methods rely heavily on domain knowledge and predefined semantic relations, limiting their flexibility and adaptability in complex driving scenarios. To overcome these challenges, this paper introduces a novel collision prediction framework named HGAT-CP, which integrates a Heterogeneous Graph Attention Network (HGAT) with a Long Short-Term Memory network (LSTM) to model the spatial-temporal interactions in scenes. First, the proposed method employs a data-driven scene graph embedding module to autonomously learn relationships between vehicles and lanes and construct flexible scene graphs. Then, the HGAT module utilizes a dual-level attention mechanism, operating at both the node level and type level, to capture spatial interactions without relying on predefined semantic rules. The LSTM module models temporal dependencies of the scene graph embeddings to improve the prediction of collision events over time. Experimental evaluations on public datasets demonstrate that our proposed method achieves state-of-the-art performance, outperforming existing methods across all metrics.

Multi-view image-based BEV (Bird's Eye View) 3D perception is gaining attention as an alternative to highcost LiDAR systems and has achieved notable success. However, there is a significant safety concern for future image-based BEV autonomous driving in low-light conditions (such as nighttime) while the limited research on BEV detectors for these scenes. In this paper, we attempt to enhance low-light BEV perception with illumination-guided feature fusion. We propose Retinex-BEVFormer, which uses illumination information generated by the Retinex theory to enhance the model's robustness to varying lighting conditions and improve detection performance in low-light scenes. Additionally, to address the illumination estimation discontinuity from multi-view images that can adversely affect detection, we propose the MVB-Retinex module, which balances illumination estimation by leveraging overlapping regions between adjacent images. Notably, our proposed method is a plug-and-play module that can be applied to any image-based BEV detector method and does not require any additional ground truth supervision. We conduct extensive experiments on the nuScenes dataset, validating our algorithm in nighttime and daytime scenes. Compared to the baseline, our algorithm achieves a 2.9% increase in mAP on the validation set with minimal computational cost, especially showing a 3.6% improvement in the nighttime scene. The experiments demonstrate that our Retinex-BEVFormer effectively improves detection performance under low light conditions and enhances performance under normal illumination, indicating increased robustness of the BEV detector.

There is a growing number of studies on the traffic control strategies of signal timings and vehicle trajectories at signalized intersections, while lane assignments are widely pre-specified and fixed. Meanwhile, existing strategies generally require a fully connected and automated vehicles (CAVs) environment. To fill up the gaps, this study contributes to a two-dimensional (spatiotemporal) control strategy by jointly optimizing traffic signals, lane settings, and vehicle trajectories at isolated signalized intersections under the mixed traffic of connected automated and human-driven vehicles. Specifically, based on the pseudo-platoons, signal timing plans and settings of approach lanes are jointly optimized by a piece-wise linear programming model. Then, vehicle trajectory control is integrated into the collaborative control framework to smooth vehicle trajectories. Three groups of numerical experiments are conducted to verify the effectiveness and efficiency of the proposed control method. Results show that the proposed algorithm outperforms the actuated control in terms of vehicle travel time under both under-saturated and over-saturated traffic conditions.

This research explores the inherent vulnerability of nonlinear vehicle platoons characterized by the oscillatory behavior triggered by external perturbations. The perturbation exerted on the vehicle platoon is regarded as an external force on an object. Following the mechanical vibration analysis in mechanics, this research proposes a vibration-theoretic approach that advances our understanding of platoon vulnerability from two aspects. First, the proposed approach introduces damping intensity to characterize vehicular platoon vulnerability, which divides platoon oscillations into two types, i.e., underdamped and overdamped. The damping intensity measures the platoon’s recovery strength in responding to perturbations. Second, the proposed approach can obtain the resonance frequency of a nonlinear vehicle platoon, where resonance amplifies platoon oscillation magnitude when the external perturbation frequency equals the platoon’s damping oscillation frequency. The main contribution of this research lies in the analytical derivation of the closed-form formulas of damping intensity and resonance frequency. In particular, the proposed approach formulates platoon dynamics under perturbation as a second-order non-homogeneous ordinary differential equation, enabling rigorous derivations and analyses for platoons with complicated nonlinear car-following behaviors. Through simulations built on real-world data, this paper demonstrates that an overdamped vehicle platoon is more robust against perturbations, and an underdamped platoon can be destabilized easily by exerting a perturbation at the platoon’s resonance frequency. The theoretical derivations and simulation results shed light on the design of reliable platooning control, either for human-driven or automated vehicles, to suppress the adverse effects of oscillations.

This paper proposes a mixed-integer program (MIP) to address a challenge issue of vehicle upgrade policy in current electric car rental market, i.e., idle luxury and high-end vehicles can be used as ordinary vehicles when the demand for ordinary vehicles is high. This model essentially is to maximize the total profit through balancing the demand and supply with a comprehensive consideration of revenue of rental operation, cost of potential demand loss, dispatching cost, energy consumption per kilometer, mileage limitation of electric vehicles (EVs), and the uncertainty of user demand for multi-type EVs. The proposed MIP model can be solved by CPLEX or Gurobi to search for the global optimal solution. Finally, real data from an electric carsharing system (ECS) with three types of EVs and 20 carsharing stations was used to validate the model. As a result, the daily increase of profit could reach 11.09% with an average of 8.08%.

Accurate train positioning is crucial for train safety. In this paper, we propose a train positioning method which fuses vison and millimeter-wave radar data. The proposed method contains two parts: loop closure detection (LCD) and radar-based odometry. The loop closure detection part fuses the convolutional neural network (CNN) features and the line features to achieve accurate key location detection. The radar-based odometry part proposes a train speed measurement algorithm using millimeter-wave radar, and combines the results of loop closure detection to further realize train positioning. Experiments conducted on the Hong Kong metro Tsuen Wan line show that our proposed loop closure detection can achieve an efficient key location detection with 98.57% precision and 99.37% recall; the speed detection method fulfills the ETCS requirements; and the relative error of the proposed train positioning method is 0.45%. Besides, the proposed method has been applied on the Hong Kong Metro TSUEN WAN line.

Lane detection plays a vital part in autonomous driving. Conventional studies rely on less robust hand-craft features, while deep learning has improved the performance of lane detection to a great extent. Different from dominant methods based on semantic segmentation, this paper proposes an end-to-end framework named DevNet, which combines deviation awareness with semantic features based on point estimation. It consists of two modules to capture more representative features by integrating information of distance deviation and angle which helps to tackle diverse driving conditions in real environments, such as dim or shiny light conditions, crowdedness, and vanishing lanes. Experiments on public datasets indicate that the proposed method achieves favorable performance when compared with the state-of-the-art methods.

In this paper, a vehicle Re-ID framework which integrates image processing and traffic flow model is developed. First, the CNN network is applied for vehicle detection and tracking and extracting attribute recognition. Particularly, attributes including vehicle color, type, make, and Re-ID feature are extracted to derive a similarity matrix between upstream and downstream vehicles. However, solely using these features could not achieve satisfaction matching accuracy. Our testing only shows a moderate accurate of around 72.3%. To further improve the Re-ID rate, this paper integrates visual information with the well-known IDM car-following mode. In our framework, IDM is first used to estimate the arrival time window for each upstream vehicle; and then with this time window derive a filter matrix which set the similarity as 0 for the matching vehicles outside the time window. Combining similarity matrix and filter matrix, the new developed Re-ID framework improves the matching rate to 95.7%. Furthermore, the proposed framework can even help identify vehicles that may have changed lanes, overtaken vehicles or driven on a sideroad. Such information is certainly valuable for future research on performance measure, traffic control, and congestion mitigation. Considering the significance of the trajectory data to nowadays traffic control and management and popularity of today’s surveillance cameras, this research certainly will contribute to the improvement of arterial traffic performance measure and efficient control.

Macroscopic arterial traffic flow modeling is crucial for signal control because most of signal control systems are relying on macroscopic models to quickly generate performance to evaluate control strategies. However, because of the randomness of vehicle speed, most of the macroscopic models are lack of capability to adequately reproduce the stochastic nature of traffic dynamics, especially for arterials with actuated signal control. To solve this problem, this research proposed a new model that incorporates a simplified shockwave model with traffic diffusion theory, which is able to reproduce the stochastic nature of traffic dynamics. The simulation results and comparison with field observed data verify that the proposed model is not only to model the traffic dynamics on arterials, but it is also able to derive signal timings of actuated intersections. Such results are encouraging since this model can be applied to a variety of real-time applications such as arterial traffic flow estimation, performance prediction and signal optimization.

In this paper, we propose a novel one-stage detector that can simultaneously detect both pedestrians and their faces. The framework is named as VikingDet for its simple but effective two-headed architecture. To tackle the challenges of person and face detection especially under surveillance cameras (e.g. low data quality, complex environments, requirements for efficiency, etc.), we make contributions in the following several aspects: 1) integrating both person and face detection into one network which current leading object detection algorithms are seldomly able to handle; 2) emphasizing detection in low-quality images. we introduce multiple thresholds for matching different sized positive samples, and set proper hyper-parameters, hence our VikingDet is able to locate small objects in surveillance cameras even of low-quality; 3) introducing a training strategy to utilize datasets on hand. Since most available public datasets annotate only people without their faces or faces without bodies, we use multi-step training and an integrated loss function to train VikingDet with these partly annotated data. As a consequence, our detector achieves satisfactory performances in several relative benchmarks with a speed at more than 60 FPS on NVIDIA TITAN X GPU, and can be further deployed on an embedded device such as NVIDIA Jetson TX1 or TX2 with a real-time speed of over 28 FPS.

With the looming promise of wireless recharging technology, electric vehicles (EVs) are going to be able to acquire energy while still in motion. This paper focuses on the optimal deployment of wireless recharging facilities on signalized arterials for EVs. To address this issue, a bi-objective model considering both traffic operation efficiency (i.e., traffic delay saving) and charging infrastructure utilization rate (i.e., electricity gain from charging) has been formulated. A modified cell transmission model (CTM) is used as a base to simulate traffic flow on an arterial with traffic signals. The cells in the CTM also serve as a potential installation site for wireless recharging facilities. The essential goal of this model is to maximize the recharging electricity for EVs traveling on arterials while maintaining low travel delay. Due to the complexity in solving the bi-objective model, heuristic approaches, such as genetic algorithm and particle swarm optimization, are employed. The numerical experiments based on real day-to-day traffic demand are executed. A Pareto set is obtained and a sensitivity analysis regarding recharging rate, investment, and minimum recharging region length is provided.

Subway short-term ridership forecasting plays an important role in intelligent transportation systems. However, limited efforts have been made to forecast the subway short-term ridership, accounting for dynamic volatility. The traditional forecasting methods can only provide point values that are unable to offer enough information on the volatility/uncertainty of the forecasting results. To fill this gap, the aim of this paper is to incorporate the dynamic volatility into the subway short-term ridership forecasting process that not only generates the expected value of the short-term ridership but also obtains the prediction interval. Four kinds of the integrated ARIMA and GARCH models are constructed to model the mean part and volatility part of the short-term ridership. The performance of the proposed method is investigated with the real subway short-term ridership data from three stations in Beijing. The model results show that the proposed model outperforms the traditional model for all three stations. The hybrid model can significantly improving the reliability of the predicted point value by reducing the mean prediction interval length of the ridership, and improve the prediction interval coverage probability. Considering the different traffic patterns between weekday and weekend, the short-term ridership is also modeled, respectively. This paper can help management understand the dynamic volatility of the subway short-term ridership, and have the potential to disseminate more reliable subway information to travelers through the information systems.

This research develops an advanced vehicle detection method, which improves the original Viola-Jones (V-J) object detection scheme for better vehicle detections from lowaltitude unmanned aerial vehicle (UAV) imagery. The original V-J method is sensitive to objects' in-plane rotation, and therefore has difficulties in detecting vehicles with unknown orientations in UAV images. To address this issue, this research proposes a road orientation adjustment method, which rotates each UAV image once so that the roads and on-road vehicles on rotated images will be aligned with the horizontal direction and the V-J vehicle detector. Then, the original V-J can be directly applied to achieve better efficiency and accuracy. The enhanced V-J method is further applied for vehicle tracking. Testing results show that both vehicle detection and tracking methods are competitive compared with other existing methods. Future research will focus on expanding the current methods to detect other transport modes, such as buses, trucks, motorcycles, bicycles, and pedestrians.

Electrification of passenger vehicles has been viewed by many as a way to significantly reduce carbon emissions, operate vehicles more efficiently, and reduce oil dependence. Due to the potential benefits of electric vehicle (EV), many federal and local governments have allocated considerable funding and taken a number of legislative and regulatory steps to promote EV deployment and adoption. With this momentum, it is not difficult to see that in the near future, EVs could gain a significant market penetration, particularly in densely populated urban areas with systemic air quality problems. We will soon face one of the biggest challenges: how to improve the efficiency for the EV transportation system? This research aims to contribute to this field by proposing an analytical model that determines a time-dependent optimal velocity profile for an EV in order to minimize the electricity usage along a chosen route by systematically considering road characteristics and real-time traffic conditions. In particular, the proposed multistage optimal control model uniquely considers the impact of the presence of intersection queues in both temporal and spatial dimensions, which has been ignored in most traditional speed control models even for internal combustion engine vehicles. In addition, to facilitate the real-time operations, an approximation model, which simplifies the optimal speed profile, is further developed to increase the computation efficiency. The testing using the field data collected from a six-intersection signalized arterial corridor shows that the optimal velocity profile can significantly save energy for an EV, and the computational efficiency of the proposed approximation model is suitable for real-time applications.