Accurate 3-D rail-track detection is vital to the perception of the railway environments for autonomous trains. However, existing methods based on monocular image cannot capture 3-D spatial features, facing challenges in detecting 3-D rail-track in turnouts and distant scenarios. This study introduces 3DRailNet, a long-range 3-D rail-track detection network using multifocal cameras. 3DRailNet consists of two modules: disparity-based feature extraction (DFE) module and long-short rail-track detection (LSRD) module. Specifically, the DFE module utilizes multifocal images to generate a disparity image and depth image, acquiring 3-D depth features to enhance the spatial information of rail-track. Based on the 3-D depth features, the LSRD module designs a detection head for long and short focal cameras to predict the 3-D position of rail-track. Experimental results demonstrate that the mean F1 score (mF1) of our proposed 3DRailNet is 83.2%, establishing it as the state-of-the-art method in this field. All these results indicate that 3DRailNet has the potential to be readily applicable in 3-D rail-track detection in railway environments.

This paper focuses on cooperative speed planning for multiple connected and automated vehicles (CAVs) traversing along intersected fixed paths. Nominally, this task is formulated as an optimal control problem incorporating logical operators to represent collision-avoidance constraints. This formulation requires solving a mixed-integer nonlinear programming (MINLP) problem, while handling non-differentiable integer variables remains challenging for gradient-based solvers. Instead of solving the MINLP, we propose a cosine-based method, a novel geometric strategy for formulating collision-avoidance constraints between CAVs. Constructing such a geometric model introduces potential approximation errors, which are mitigated by fitted correction terms designed to compensate for geometric deviations and refine the distance calculation. We propose a simulation-based planner to provide the speed profile with the globally optimal passing order, serving as a warm start for the solver. A lightweight iterative optimization strategy is also adopted to enhance robustness. Additionally, we propose a fault-tolerant strategy to ensure both system safety and operational efficiency. Extensive simulation results verify the proposed method, and comparative experiments demonstrate its efficiency.

This study proposes a risk-tolerant dispatch approach for a fleet of autonomous mining trucks in an open-pit mine, leveraging early signs to reduce the impact of potential failures that may or may not occur later. Unlike traditional methods that ignore these early signs or wait until a failure has actually happened, our approach proactively plans for both possible outcomes without relying on probabilities. We propose a Y-shaped solution structure composed of a shared trunk that covers the period before it becomes clear if the failure will occur and two separate branches that address the final scenarios. We formulate the dispatch problem as a mixed-integer linear program and solve it via Gurobi. To facilitate the solution process with Gurobi, an evolutionary algorithm is adopted to explore the solution space for a good initial guess. A high-performance discrete-event simulator is embedded in the cost function evaluation module of the evolutionary algorithm for quickly selecting qualified solution candidates. By integrating both the failure and non-failure scenarios into one unified plan, we avoid extreme risk-taking or undue conservatism, ensuring stable operational performance. Simulations and field trials at a real open-pit mine confirm that this risk-tolerant approach effectively manages failure risks when early signs are available.

Path tracking control is essential for ensuring the safe and efficient operation of autonomous trucks, but traditional methods often struggle with nonlinear vehicle dynamics. While deep reinforcement learning (DRL) approaches are model-free, they may lack the stability and interpretability required for reliable deployment. This study presents a hybrid control framework that combines Pure Pursuit (PP) with Proximal Policy Optimization (PPO) to enhance tracking accuracy and robustness. PP provides baseline stability and interpretability, while PPO refines control actions by optimizing policy gradients, ensuring better adaptability to nonlinear dynamics and complex driving conditions. An adaptive look-ahead mechanism, responsive to speed and curvature, dynamically adjusts preview distances using PPO-generated coefficients, facilitating early corrections during high-speed turns and enabling greater precision on sharp curves. A fusion training method, leveraging high-reward initialization and a decreasing learning rate, supports efficient exploration and stable convergence. The approach was validated in a high-fidelity simulation environment using PreScan, Simulink, and ROS, along with real-world experiments on a proportionally scaled intelligent vehicle chassis, demonstrating notable improvements in path tracking accuracy and robustness across varied path profiles.

Accurate detection of 3D objects is vital to the perception of the railway environments for the safe operation autonomous trains, particularly given the complexity of railway environments and the challenges in detecting objects of variable sizes and of distant objects. This study introduces RailFusion, a LiDAR-Camera fusion network for integrating multi-modal features. RailFusion consists of two main modules: Cross-Domain Feature Extraction (CDFE) and Multi-Modal Fusion (MMF). Specifically, the CDFE module is designed with a novel feature extraction method to enhance cross-domain features interaction by utilizing LiDAR spatial depth and image semantic information. The MMF module uses deformable attention for aligning and fusing multi-modal features. Further to this, the channel normalization fusion is proposed to assign channel weights. Experimental results show that the mean average-precision (mAP) of our proposed RailFusion is 57.2%, which is 8.4% higher than the baseline 3D object detection network BEVFusion. Moreover, the results show that RailFusion is applicable to long-range detection as well as for detecting varying sized and short-range objects. All these indicate that RailFusion has the potential to be readily applicable in 3D object detection in railway environments.

Addressing the crucial challenge of autonomous navigation for unmanned ground vehicles (UGVs), this paper presents a dual-layer path planning method integrating Probabilistic Roadmap (PRM) and Proximal Policy Optimization (PPO). Combining global guidance with local optimization, this approach effectively mitigates the shortcomings of traditional path planning methods such as blindness and local optimality, thus enhancing the efficiency and feasibility of path planning. Specifically, we propose a PRM-RL dual-layer path planning framework that employs the PRM algorithm to generate sub-goals for guiding reinforcement learning exploration, thereby improving training efficiency. Simultaneously, we utilize the PPO algorithm to optimize paths, considering vehicle kinematics and introducing soft constraints to ensure smoother paths adaptable to diverse application scenarios. The superiority and practicality of our method are validated through ablation experiments and comparative experiments, offering a reliable path planning solution for autonomous navigation of UGVs.

In rail transit autonomous driving scenarios, real-time three-dimensional (3D) reconstruction is crucial for understanding scenes and ensuring the safety of the driving environment. Urban rail trains, with their substantial weight and long braking distances, necessitate an extended forward perception range in 3D space. To address this challenge, this paper proposes a method for forward long-distance 3D scene reconstruction tailored for rail transit scenarios based on occupancy networks. Firstly, a 3D feature representation method using three mutually perpendicular spatial planes is proposed to mitigate the high computational complexity of spatial voxel features. Secondly, considering the characteristics of forward binocular vision in rail transit scenarios, we employ self-attention and cross-attention mechanisms to fuse features between different images. Thirdly, due to the limitations in projection distance of the Light Detection and Ranging (LiDAR) point cloud used for supervision, we introduce a method to generate long-distance dense ground truth during the training stage. By pioneering the application of occupancy networks in rail transit scenarios, this approach significantly extends the forward perception range of autonomous driving trains, achieving an impressive 77.79% mean Intersection over Union (mIoU) accuracy.

Open-pit mining areas have special geological structures, complex road networks, multi-rotation sections and poor road conditions, which bring many challenges to the operation of mining autonomous transport vehicles. Due to the large size and high control difficulty of mining autonomous transport vehicles, poor control effect and inaccurate steering mechanism implementation are prone to occur during the driving process, which leads to the vehicle departure from the reference path. To ensure the safety of autonomous operation in intelligent mine, this paper proposes a method for predicting driving departure of mining autonomous vehicles based on Gated Recurrent Unit (GRU). Firstly, the vehicle's historical trajectory data is obtained via on-board sensors, followed by data cleaning and normalization processes. Key features are extracted, and a vehicle driving scene recognition module based on a GRU-based network is designed using deep learning. Subsequently, a GA-seq2seqGRU trajectory prediction module is constructed utilizing the scene recognition results, and the Genetic Algorithms (GA) is employed to tune the network hyper-parameters. Based on the predicted trajectories, the overall departure risk is calculated using two departure judgment methods based on cross-lane time and predicted lateral deviation, which are mapped to the departure level. Simulation experiments demonstrate that the accuracy of the driving scene recognition model in the proposed method in this paper reaches 0.9721, which is better than the 0.9585 of the Long Short Memory Neural Network (LSTM) model, and the trajectory prediction model based on the results of the driving scene recognition has a smaller RMSE value than the LSTM, GRU, and GA-seq2seqLSTM models when the prediction time domains are 1s, 2s, 3s, 4s, and 5s, and the departure detection module The average time consumed is 0.142ms.

Autonomous transportation systems, which have reconstructed open-pit mining operations, depend on accurate and real-time obstacle detection in challenging environments. Existing methods often exhibit limitations in accuracy due to their reliance on traditional image-based techniques, leading to errors and incomplete results. To address these issues, we developed a vision-based 3D object detection algorithm designed for open-pit mining environments. Our approach leverages the BEVDepth model, enabling accurate 3D object recognition using monocular camera input. Moreover, camera parameters are integrated to enhance resilience and flexibility across diverse mining environments. Last, our algorithm was implemented on a newly customized open-pit mining dataset. The experimental results verify the algorithm efficiency by attaining a high mean Average Precision score (m$A$P) of 67% while offering real-time performance with inference times as short as 25ms per frame. It enables accurate and efficient obstacle detection in autonomous mining vehicles and contributes to the development of safer and more productive mining operations.

Autonomous vehicles in mining areas undertake substantial production tasks and are prone to various faults during operation. Early detection of abnormalities, along with timely fault warnings and diagnoses, can enhance transportation safety and increase vehicle turnout rates. This study utilizes driving data from autonomous vehicles in mining areas and considers the characteristics of unstructured road scenes. The driving area is segmented into distinct intervals, and Kullback-Leibler (KL) divergence is applied within each interval to detect anomalies in the vehicle's lateral deviation during operation. Experimental results demonstrate that the proposed method achieves an anomaly detection accuracy of 91.4%, with a false negative rate of 8.3% and a false positive rate of 8.7%.

On roads, both vehicles and pedestrians will leave traces after passing by. Slight traces will not cause any trouble to the passing vehicles and pedestrians subsequently. However, there are certain potholes in the road that have been formed by repeated crushing of vehicles. It may cause mechanical damage to following vehicles and lead to accidents as vehicles get stuck in the potholes. Thus, the segmentation of negative obstacles is crucial for the safe driving of self-driving vehicles. This paper proposed an AWNet neural network to this end, and quantitative analyses were conducted based on the publicly available dataset DRNO. Specifically, a random cut-and-joint module (RCJ) was proposed to augment the dataset. The crossentropy loss function was modified by adding an adaptive weight module, i.e., the adaptive weight cross-entropy loss function. It allows the model to focus on negative obstacles during implementation. Furthermore, this paper proposed a post-processing module for adaptive viewpoint conversion based on depth maps, which is capable of fusing depth maps and RGB images to convert the front view into a BEV viewpoint. The mAcc for Negative obstacle semantic segmentation can reach$78.39\%$, the mIoU can reach$73.21{{\% }}$, the mF 1 can reach$79.23{{\% }}$. The experimental results reveal that the AWNet is able to state-of-the-art performance compared with other networks.

Real-time and full-sample vehicle origin-destination (OD) information is essential for traffic management and control in urban road network. However, the low coverage of automatic vehicle identification (AVI) detection devices leads to difficulty in estimating OD. As an emerging traffic data, the trajectories of connected vehicles (CVs) can effectively provide information on their origin and destination. To this end, this paper presents a framework of an autoencoder network utilizing feature transfer to estimate urban dynamic OD based on the characteristics of two data sources. Specifically, a generative adversarial network is introduced to learn high-dimensional feature that is domain-invariant in two data domains. In addition, a pre-training fine-tuning approach is proposed to transfer knowledge pretrained from CV data to the limited AVI observation for OD imputation. Finally, the model was subjected to a real-world road network test. The results showed that for all OD flows the relative error was 11.23 vehicles/30 minutes, which outperformed baseline models, including popular neural networks and existing estimation models for multi-source data fusion. Furthermore, the model’s robustness to external factors, such as observation conditions and data quality, was examined. The results demonstrated that the model consistently delivers satisfactory estimation performance across a diverse range of conditions.

Rail-track detection is a crucial function for an active obstacle avoidance system in trains. However, existing methods face challenges in effectively detecting rail-tracks, particularly in turnout scenarios. This study introduces a novel rail-track detection approach using a key-point estimate network. The network treats the rail-track as a pair and constructs a dedicated model for detection. Additionally, a pseudo-attention mechanism leverages the detection output from previous stages, enabling the network to focus on the rail-track region. Also, a dislocation assignment mechanism is proposed to address label assignment confusion at turnouts. Moreover, a rail-track generalized IoU is also introduced, treating the rail-track as a pair and adds a correction term to enhance detection performance. Experimental results demonstrate that the proposed method achieves a remarkable mF1 score of 69.42%, establishing it as the state-of-the-art (SOTA) in this field. Furthermore, the effectiveness of the proposed method has been validated and applied in real-world testing on the Hong Kong Metro Tsuen Wan Line.

Rail track segmentation is key to environmental perception of autonomous train. However, due to the complexity of railway track environment, critical issues such as the detection of rail tracks with different curvatures remain to be overcome. In this study, a novel architecture called FarNet is proposed for long-range railway track point cloud segmentation. The proposed FarNet is mainly divided into three parts, i.e., spherical projection, attention-aggregation network and results refinement. Specifically, spherical projection converts the LiDAR point cloud into a pseudo range image, and attention-aggregation network enables railway track detection using the pseudo range image. Furthermore, in the attention-aggregation network two components, i.e., spatial attention module and information aggregation module, are proposed to enhance the capability of rail track segmentation. Last, the results refinement helps further filter out the noise points after segmentation. Experimental results show that the proposed FarNet achieved 98.0% mean intersection-over-union (MIoU) and 98.9% mean pixel accuracy (MPA) for rail track segmentation.

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.

Queue length is a crucial measure of intersection performance. Probe vehicles (PVs) with advanced sensors are capable of recording vehicle trajectories that can be used to estimate queue length, a technique of which has received considerable attention in the past decade. Noticeably, this technique usually requires high PV penetration rates (e.g., above 25%) in order to ensure estimation accuracy. Though the PVs are expected to increase, their penetration rate will still remain relatively low in the near future. Meanwhile, the initial queue length is another important factor that directly relates to queue dynamics at each cycle. However, most of the studies failed to adequately account for the effect of the initial queue on cyclic queue length estimation. To address the above challenges, this paper proposes a cycle-based end of queue estimation method using sampled vehicle trajectory data under relatively low penetration rates. Two major steps are involved: first, vehicle arrival process is modeled as a certain distribution in line with traffic conditions and an expectation maximum (EM) procedure is employed to estimate the arrival rate of each cycle; then, both ends of the queue and initial queue are estimated at each cycle based on shockwave theory. Microscopic traffic simulator VISSIM is utilized to examine the performance of the method. The experimental results reveal that the cycle-based end of the queue can be estimated with desirable accuracy in different scenarios, e.g., undersaturated, oversaturated, and queue spillback conditions. The comparison with the state-of-the-art methods further helps to verify the advantage of the method, especially under low-penetration-rate conditions.

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.

In this paper, we present an adaptive signal control scheme to prevent intersection traffic blockage resulted from vehicle queue spillover. A method to identify vehicle queue spillover condition through simplified shockwave analysis is developed. Instead of measuring the vehicle queue length or locating the end of queue directly, this method relies on the vehicle speed which is more feasible to measure in practice. The adaptive traffic signal control scheme is designed to prevent potential intersection traffic blockage, and adaptively allocates green time to appropriate signal phases. At the end, a simulation study is carried out to evaluate the proposed adaptive control scheme. The results show that the scheme can effectively prevent intersection traffic blockage and significantly improve the performance of the intersection in terms of vehicle delay.

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.