The development of the Internet of Vehicles (IoV) offers significant opportunities to enhance the traffic management system based on connected vehicles (CVs), while accurate traffic arrival rate estimation is critical for the dynamic evaluation and optimization of signalized intersections. Existing CV-based methods, however, are constrained to single-stream estimation that assumes first-in-first-out (FIFO) discipline, overlook initial queues, and deteriorate sharply when CV penetration is low or data are spoofed. To address these limitations, this study proposes a JO-MAP (JOint Maximum A Posteriori) method that jointly estimates cycle-based arrival rates of multiple traffic streams under both undersaturated and oversaturated conditions. The key innovations include a joint weighted likelihood function that treats each queued CV as an independent observation, eliminates FIFO assumptions, and explicitly accounts for the initial queues, and a joint Bayesian prior that embeds historical CV sample-size information for enhanced accuracy even with sparse real-time CV data. Comprehensive simulation and field experiments show that JO-MAP produces reliable estimates under different penetration rates, arrival patterns, and traffic volume levels, achieving 100% estimation success and less-than 4 veh cycle-level error with only 5% CV penetration. The feature of joint estimation makes the method less demanding for the penetration rate of CVs and more robust to noisy/spoofing data compared to baseline methods, limiting the error increase to 1.2 veh under deliberate spoofing attacks. Besides, JO-MAP reduces average vehicle delay by 12%–20% when integrated into adaptive signal control, demonstrating its potential for IoV-enabled traffic management.

Robotics and automation is one of crucial trend in smart manufacturing to improve production efficiency. Au-tomated guided vehicles (AGVs) are a type of mobile robot used for material handling and have become widely utilized to achieve transportation automation. The usage of multiple AGVs introduces potential risks, such as traffic conflicts and safety risk. To meet high production demands, numerous shop floors are set up for large-scale manufacturing. Thus, reasonable and efficient AGV scheduling is vital for real-world operations. This paper proposes an efficient and scalable prioritized planning algorithm for large-scale multiple-AGV scheduling problem in manufacturing. The algorithm sequentially addresses two primary sub-problems: job assignment and conflict-free routing. The results of job assignment dictate the routes taken by the AGVs. In job assignment, jobs are allocated sequentially based on their pickup times. In conflict-free routing, AGV priorities are predefined, ensuring that higher priority AGVs maintain their movement while adjustments are made only to lower priority AGV plans when conflicts arise. Simulation is conducted on two real shop floor layouts and demonstrates the effectiveness and high efficiency of proposed algorithm. Even in a large-scale layout with 500 jobs and 20 AGVs, the computation time is only around 21 seconds.

Human-Robot Collaboration (HRC) is essential for enhancing productivity and flexibility in smart manufacturing, which poses requirements on accurately predicting the future movements of human operators, especially the trajectories of their upper limbs. However, existing model-based studies on human manipulation prediction lacks consideration of stochasticity and variability while the emerging deep learning-based methods are demanding on data size, which yet makes real-time deployment challenging. Therefore, combining the advantages of both model-based and deep learning-based methods, a method for predicting human arm motion, specifically, the position of a worker's wrist in less than 0.5 second, is proposed by hybridizing a Long Short-Term Memory (LSTM) network with an Inverse Kinematics (IK) model. Using historical coordinate sequences of the wrist joint in three-dimensional space in the past multiple frames as input, a neural network is trained to output the predicted coordinates of the wrist joint for the next frame. Then IK (Inverse Kinematics) is used to calculate the arm's motion trajectory based on the predicted wrist coordinates. As the predicted wrist coordinates are sequentially used as the input for the next prediction cycle, the prediction is realized over a sliding time window. Evaluation was conducted using both proprietary and open datasets, results demonstrated that our LSTM-IK method achieved high prediction accuracy, with an average distance error of approximately 5 cm, and can adapt to various task scenarios and individual differences. Additionally, comparison with ground truth illustrated the model's ability to handle complex motion patterns, even with partial occlusions or rapid movements.

As an important part in smart manufacturing under Industry 4.0 era, human-robot collaboration (HRC) features the interaction between human operators and machines, which makes the research of human fatigue come into sight. However, most existing studies on human fatigue or efficiency detection are realized using detectors and models from bioelectronics, whose intrusive detection and decoding of electromyographic signal limits the generality and applicability of such methods. Therefore, this study proposes a human fatigue extraction method based on video data. A new dataset on human manipulation is established by collecting video data of assembly operations to simulate the working status of human operators under smart manufacturing environment. With human skeletal data extracted from the video using a machine learning-based pose extraction tool, MediaPipe, a spatiotemporal analysis for critical skeleton points is implemented for working status categorization and learning using a stochastic gradient descent (SGD) classifier. In this way, the duration taken to complete an assembly task can be extracted as the operation time using the trained SGD classifier, and thus the time-varying operation time series data are obtained to show the trend of human fatigue level. An accuracy of 98.3% is obtained for working status identification for the dataset. Several quantitative indicators like pearson correlation, R-squared value, root mean squared error (RMSE), and Fréchet Distance, are used to evaluate the accuracy of both the extracted operation time and its time-varying curve as compared to the ground truth, with satisfactory results showing the effectiveness of the proposed method.

Network signal coordination control is a crucial means to improve the traffic operation efficiency of the overall roadway network. Accurate identification of critical paths does play an important role in determining the scope of network coordination control. Therefore, this paper proposed the definition of critical path from the perspective of traffic control and management. Under the detection environment of connected vehicle (CV), a comprehensive quantitative indicator system for path criticality evaluation from three aspects, supply side, demand side and operation side, which are arranged in the form of a tower structure. A critical path identification method (CPIM) was then proposed based on the analytic hierarchy process (AHP) theory, which was hereinafter referred to as AHP-CPIM. In order to evaluate the feasibility and effectiveness of the proposed method, a case study set in an urban network in Tongxiang, Zhejiang Province in China, is conducted through simulation models built through VISSIM and Synchro. Two scenarios were set, one is coordination control based on the coordination subarea obtained from Synchro (namely without critical path identification), and another one is coordination control with critical paths obtained from AHP-CPIM. Results showed that, compared with the control of Synchro and Multiband method under the scenario of coordination control without critical path identification, network signal coordination control optimization based on AHP-CPIM improved about 37.9% and 35.9% in average delay, respectively, justifying the effectiveness of CV-driven critical path identification for network signal coordination control.

For a long period, automated vehicles (AVs) or vehicle platoons will coexist with human-driven vehicles (HDVs) in heterogeneous traffic flow, where the cut-in maneuver of human drivers can be frequently expected. In this paper, to understand and simulate the driver decisions on whether to continue the cut-in and when to execute the lane-change during the cut-in process, we propose a two-layer prediction-based decision model by integrating a dynamic prediction module, a continuity decision module, and an execution decision module. To our best knowledge, this is the first study to model the driver decision behavior of the cut-in process. Cut-in experiments are conducted to collect the decision and control data of drivers under one-and two-target-vehicle scenarios, which both include sixty sub-scenarios with different initial velocities, accelerations, or positions of the vehicles. We prove the effectiveness of the proposed model in simulating the driver decision behavior of the cut-in process by comparing the experimental and simulation results under various scenarios over different subjects. Besides, we analyze the effects of some model parameters on the model performance to show their ability to represent different driving styles.

Automated Guided Vehicles AGVs play a vital role in enhancing productivity and efficiency within factory environments. However, their safe and effective operation heavily relies on the ability to navigate through complex spaces while avoiding obstacles. The significance of obstacle avoidance in AGV systems is emphasized, considering its impact on ensuring smooth material flow, minimizing collision risks, and optimizing production processes. The existing state of obstacle avoidance applications in factory settings reveals certain limitations and challenges. Current research and industrial implementations often rely on rule-based approaches or predefined paths, which may not adequately adapt to dynamic environments or unexpected obstacles. Additionally, some methods lack the ability to handle diverse obstacle types or efficiently plan optimal paths, leading to sub-optimal navigation or reduced throughput. In response to these challenges, this study proposes a novel approach for dynamic obstacle avoidance of AGVs based on deep reinforcement learning. By leveraging the Deep Deterministic Policy Gradient (DDPG) model, the AGV learns to make real-time decisions and navigate through dynamic obstacles effectively. The integration of deep neural networks with the actor-critic framework enables the AGV to learn and adapt optimal policies for obstacle avoidance in real-time, overcoming the limitations of rule-based methods. Simulation experiments are conducted to validate the performance and feasibility of the proposed approach. The results demonstrate that the DDPG-based method allows the AGV to successfully navigate through both dynamic and static obstacles in a dynamic environment, improving safety and efficiency in intelligent manufacturing applications.

Signal phase optimization at signalized intersection is of great importance to urban traffic control and management yet is very challenging. The traditional approaches for signal phase optimization heavily rely on the traffic engineering practitioners’ experience. To tackle these challenges, a novel data-driven method is proposed to realize signal phase optimization and recommendation solely using limited amount of real signalized intersection samples. Firstly, all of discrete features related to signal phase design, encoded by one-hot representation, are sampled by the Gumbel-SoftMax distribution, which is a continuous approximation to a multinomial distribution. With this approximation distribution, the generative adversarial network (GAN) is applied to produce the most acceptable signal phase samples among all acceptable choices, dealing with the problem of insufficient samples and uneven sample distribution in real word. Thirdly, a decision-tree based classifier is established to realize signal phase recommendation automatically. We conducted extensive experiments to evaluate our proposed method on three cities in China, including Beijing, Tongxiang and Chaozhou. The experimental results showed that the proposed method could effectively improve the signalized intersection operation efficiency. Moreover, the proposed method has already been deployed in several cities, and it successfully keeps serving hundreds of signalized intersections. This confirms that SPSRec is a practical and robust solution for large-scale real-world signal control services.

Vehicle platoons (VPs) are groups of vehicles driving together with a short inter-vehicle gap and a harmonized velocity. For a long period, the VPs and human-driven vehicles (HDVs) will coexist in mixed traffic flow, where the cut-in maneuver of the HDVs towards the VPs can be frequently expected. In this paper, to handle such cut-ins, we propose an intention prediction-based control method for the VPs by considering the tradeoff between the platoon integrity and traffic safety. Particularly, the proposed method is designed to prevent as many cut-ins as possible while taking care of the road safety. It consists of a cut-in prediction part, including intention and trajectory prediction algorithms, and a finite state machine (FSM)-based predictive control part, including a high-level FSM and a low-level predictive control. Driver-in-the-loop experiments were conducted in the VP-based driving scenarios to train the intention prediction algorithm and test the proposed method. We show the results detailing the control behavior of the proposed method in a no cut-in test, a mandatory cut-in test, and three discretionary cut-in tests. The results demonstrate that the proposed method can predict the cut-in intention of human drivers in real time. Besides, according to the prediction results, the proposed method can prevent cut-ins for the VPs while taking care of the road safety.

Although considerable methods have been developed for the performance evaluation of signalized intersections using sampled vehicle trajectories, most of them aim at estimating a single parameter and cannot describe the entire arrival–departure process of the traffic flow. This significantly constrains the application of these methods for a comprehensive evaluation and efficient optimization of signalized intersections. In this paper, we propose a cumulative flow diagram (CFD) estimation and prediction method using sampled vehicle trajectories. It can be used to calculate multiple performance measures—traffic volume, queue length, average delay, and total delay—based on the estimated CFD for the current signal timing plan. Concurrently, it can be further employed for signal control optimization based on the predicted CFDs for candidate signal timing plans. The core idea of the proposed method is to generate the cumulative arrival curve based on the arrival characteristics of the sampled vehicles, and then fit the queue leaving points to obtain the cumulative departure curve. Thereby, given the current or any candidate signal timing plan, we can estimate or predict the CFDs by updating the sampled vehicle arrivals. The proposed method is evaluated using both simulation and empirical data. The simulation results yield that the average estimation error of the four performance measures is 10.3% under a real-world level penetration rate of 10%. Meanwhile, similar accuracies are achieved for the CFD prediction. The empirical results show that under a penetration rate of 8.6%, the estimation errors of the traffic volume and queue length are 2.7% and 3.3%, respectively.

Intersection queue length estimation using high-resolution probe vehicle trajectory data has received increasing attentions in recent years. Existing methods for cycle-based queue length estimation still face the challenge of low and/or unstable estimation accuracies under the condition of sparse vehicle trajectory data, i.e., there is no greater than one vehicle trajectory per cycle on average. To address this challenge, this study proposed a novel approach for cycle-based queue length estimation by fusing real-time and historical probe vehicle trajectory data, through a statistical parameter estimation method, i.e., maximum likelihood estimation (MLE). With known signal timing information, firstly, the historical probe trajectory data are used to acquire the arrival flow rate distribution over the entire study period. Then, a likelihood function of queue length is derived by fully exploiting real-time traffic flow information provided by the queued and non-queued probe vehicles. Finally, the MLE method is adopted to estimate the cycle-based queue lengths with the maximum probability. The proposed approach is verified using both simulation and empirical data. Results indicate that precise estimation for cycle-based queue lengths can be realized based on sparse vehicle trajectory data, while showing superiority to a representative existing method. The proposed method is basically an offline method, but it can also work in an online manner if provided a priori arrival distribution either acquired from historical probe vehicle trajectory data or a theoretical assumption.

The cycle-based volume is critical for traffic state estimation and signal control optimization at signalized intersections. Traditional volume estimation mainly depends on fixed detectors represented by loop detectors, but limited spatial coverage and detection failure are also prominent. With the development of vehicle positioning, smartphone-based navigation, and connected-vehicle technologies, massive high-resolution trajectory data have recently become available, which can provide rich and timely information on the traffic arrival and departure processes at signalized intersections. Hence, the studies utilizing trajectory data for estimating the queue length and traffic volume at intersections has received increasing attention in the past few years. However, the most existing studies have demanded a comparatively high penetration rate and adopted site-specific assumptions for unsteady arrival patterns. In contrast, this paper solely used trajectory data for cycle-based flow estimation through a generic hybrid method that combined a probabilistic model and shockwave theory to maximize the utilization of limited captured trajectories, especially under a low penetration rate. In this method, within each cycle, the volume of stopped vehicles is estimated based on the shockwave theory, while the volume of non-stopped vehicles is modeled as a parameter estimation problem of a time-dependent constrained Poisson distribution, where the time headway correspondingly obeys an M3 distribution. The cycle-based volume is solved by a maximum likelihood estimation using an expectation-maximization procedure. An empirical case study was conducted with various signal timing schemes and the results showed satisfactory robustness with an accuracy of more than 90% under a penetration rate of 7.6%.