Large-scale pavement distress inspection has gained attention recently. Traditional methods, involving dedicated vehicles and professionals, are effective for small-scale evaluations but are labor-intensive for extensive areas. Logistics vehicles, equipped with driving recorders, provide a vast resource of street view images covering urban roads, capturing pavement distress data. This paper proposes a novel framework leveraging logistics vehicle data for rapid distress detection and tracking. A deep convolutional neural network, enhanced with a dual-layer routing attention mechanism, improves detection precision for minor distresses. The optimized Boundary Box Regression (BBR) loss function increases accuracy for common distresses like cracks. A three-stage distress matching algorithm, based on an attentional graph neural network and adjacent-local-area matching, removes duplications and tracks distress deterioration. The Bernoulli function assesses minimal sampling frequency for road segments. Validated in Shanghai, this method achieves 79.6% mean Average Precision (mAP) and a 75.66% matching rate, enabling daily updates for timely maintenance decisions.

Reinforcement Learning (RL) has demonstrated excellent decision-making potential in platoon coordination problems. However, due to the variability of coordination goals, the complexity of the decision problem, and the time-consumption of trial-and-error in manual design, finding a well performance reward function to guide RL training to solve complex platoon coordination problems remains challenging. In this paper, we formally define the Platoon Coordination Reward Design Problem (PCRDP), extending the RL-based cooperative platoon coordination problem to incorporate automated reward function generation. To address PCRDP, we propose a Large Language Model (LLM)-based Platoon coordination Reward Design (PCRD) framework, which systematically automates reward function discovery through LLM-driven initialization and iterative optimization. In this method, LLM first initializes reward functions based on environment code and task requirements with an Analysis and Initial Reward (AIR) module, and then iteratively optimizes them based on training feedback with an evolutionary module. The AIR module guides LLM to deepen their understanding of code and tasks through a chain of thought, effectively mitigating hallucination risks in code generation. The evolutionary module fine-tunes and reconstructs the reward function, achieving a balance between exploration diversity and convergence stability for training. To validate our approach, we establish six challenging coordination scenarios with varying complexity levels within the Yangtze River Delta transportation network simulation. Comparative experimental results demonstrate that RL agents utilizing PCRD-generated reward functions consistently outperform human-engineered reward functions, achieving an average of 10% higher performance metrics in all scenarios.

Vehicle-to-Everything (V2X) collaborative perception bolsters the performance of autonomous vehicles by overcoming occlusion challenges and expanding their sensing range. However, current methodologies frequently overlook the intrinsic uncertainties associated with localization inaccuracies, asynchronous measurements, and diverse agent models. Such uncertainties may weaken fusion reliability, leading to performance worse than single-vehicle perception. To address this pivotal challenge, we introduce BELT-Fusion, a cohesive probabilistic framework tailored for reliable V2X late fusion. Our framework offers two notable advantages. The first is explicit agent-level uncertainty modeling, where classification uncertainty is captured via evidential deep learning and regression uncertainty via Bayesian neural networks. This capability allows task-specific reliability assessments to be effortlessly incorporated into existing object detectors. Second, our framework introduces an uncertainty-aware adaptive fusion representation. This representation dynamically guides object selection and weight allocation based on measurable fusion-level uncertainty, ensuring reliable fusion results without retraining and enabling plug-and-play functionality. To validate BELT-Fusion’s efficacy, we conducted evaluations focusing on 3D object detection in both real-world and simulated scenarios using the OPV2V and DAIR-V2X datasets. BELT-Fusion improved [email protected] by 7.16% in noisy settings and 3.84% under ideal conditions over uncertainty-agnostic baselines, demonstrating its robustness under challenging and noisy conditions. Our code will be available athttps://github.com/ZhiguoZhao/BELT-Fusion

The increase in highway mileage and lifespan is driving up the demand for road maintenance. With most research focusing on corrective maintenance, remedial maintenance(such as sealing and patching) optimization is understudied. Oriented toward remedial maintenance, data-driven models often fall short due to difficulty in establishing and implementing the model under complex road conditions, while the experts’ decision lacks consistency amidst multifaceted factors. To address this gap, this paper proposes a fine-grained maintenance decision model that combines data-driven methods with expert knowledge through Reinforcement Learning from Expert Feedback (RLEF). The experts’ experience introduced in decision-making model could improve the engineering application ability of decisions. The research uses a pavement performance prediction model as the environment and applies reinforcement learning to optimize strategies in the decision model. Additionally, the model integrates multidimensional expert feedback into reward functions to better understand ambiguous decision rules. Real-world data validation demonstrates that the RLEF model can adapt to engineering scenarios and applications better as well as achieve superior cost-effectiveness.

This paper presents a novel, high-performance multi-vehicle detection and tracking (MVDT) framework to extract vehicle trajectories from roadside 3D point clouds. First, we developed a vehicle detector called the deep evidential occupancy grid model. This model uses PointNet to extract features from vertically organized point cloud pillars, generating a bird’s-eye view (BEV) grid feature map, followed by a 2D convolutional neural network to capture both local and global spatial features. The detection head uses a Dirichlet distribution to gather “evidence” from gathered features that indicate grid occupancy or vacancy, quantifying the model’s “confidence” through a probabilistic representation of uncertainty. An adaptive post-processing method is applied to output detection results with associated uncertainty. Second, we integrated uncertainty into the tracker, developing an uncertainty-aware multi-vehicle tracking (UMVT) model. This model manages the trajectory initialization and termination strategies, enhancing tracking robustness in complex scenarios. Comprehensive experiments on two real-world datasets, DAIR-V2X and V2X-Real, demonstrate that the proposed MVDT framework outperforms state-of-the-art methods in roadside perception, achieving improvements in both detection and tracking tasks. The effectiveness has also been validated using point cloud data from the Shanghai-Nanjing Expressway, showcasing its excellent practical applicability.

Black-box decision-making algorithms based on deep learning and reinforcement learning have demonstrated significant advancements in autonomous vehicles (AVs), but still encounter challenges due to unpredictable failures in long-tail scenarios. The efficient evaluation of the safety of these algorithms is essential for the further development of AVs. This paper introduces a novel testing and evaluation method that leverages subset simulation (SuS) with adaptive Markov chain Monte Carlo (ApMCMC). The method is designed to facilitate the occurrence of rare events and expedite the probability assessment process, thereby enabling rapid evaluation of the failure probability of deep reinforcement learning (DRL)–based end-to-end decision-making algorithms. The car-following scenario is used as a case study to demonstrate the efficacy of our proposed method and four state-of-the-art DRL-based algorithms are trained as evaluation objects. Experimental results indicate that our method maintains evaluation errors within 5%. Compared with the naive Monte Carlo method and other existing evaluation techniques, our approach significantly enhances evaluation accuracy, reduces variance, and improves confidence levels, while accelerating evaluation efficiency by 5-200 times. Additionally, our method can easily handle end-to-end black-box decision-making systems and exhibits the adaptability to be seamlessly extended and applied to other scenarios.

Pavement distress, manifested as cracks, potholes, and rutting, significantly compromises road integrity and poses risks to drivers. Accurate prediction of pavement distress deterioration is essential for effective road management, cost reduction in maintenance, and improvement of traffic safety. However, real-world data on pavement distress is usually collected irregularly, resulting in uneven, asynchronous, and sparse spatial-temporal datasets. This hinders the application of existing spatial-temporal models, such as DCRNN, since they are only applicable to regularly and synchronously collected data. To overcome these challenges, we propose the Spatial-Temporal Graph Autoregression Network (STGAN), a novel graph neural network (GNN) model designed for accurately predicting irregular pavement distress deterioration using complex spatial-temporal data. Specifically, STGAN integrates the temporal domain into the spatial domain, creating a larger graph where nodes are represented by spatial-temporal tuples and edges are formed based on a similarity-based connection mechanism. Furthermore, based on the constructed spatiotemporal graph, we formulate pavement distress deterioration prediction as a graph autoregression task, i.e., the graph size increases incrementally and the prediction is performed sequentially. This is accomplished by a novel spatial-temporal attention mechanism deployed by the proposed STGAN model. Utilizing the ConTrack dataset, which contains pavement distress records collected from different locations in Shanghai, we demonstrate the superior performance of STGAN in capturing spatial-temporal correlations and addressing the aforementioned challenges. Experimental results further show that STGAN outperforms baseline models, and ablation studies confirm the effectiveness of its novel modules. Our findings contribute to promoting proactive road maintenance decision-making and ultimately enhancing road safety and resilience.

Full-sample vehicle trajectories collected by roadside sensors are pivotal for advancements in active traffic management and vehicle-highway automation applications. However, sensor inefficiencies and environmental factors often lead to incomplete and noisy data, impeding practical applications. To overcome this hurdle, we propose a data-driven and kinematics-aware method for reconstructing full-sample vehicle trajectories. It employs an encoder-decoder architecture, where the data-driven encoder utilizes graph-based map modeling to capture road network characteristics and models multi-vehicle interactions based on real-world driving attention patterns. The model-driven decoder integrates conditional variational autoencoder with a vehicle kinematic model, to ensure that the generated trajectories adhere closely to vehicle kinematic principles. Experiments on large-scale trajectory datasets demonstrate that our approach exhibits good performance in trajectory reconstruction. Furthermore, case studies on the DAIR-V2X dataset indicate that our approach could improve raw trajectory data quality and downstream prediction accuracy.

Cooperative perception, using vehicle-to-everything (V2X) technologies for perceptual data sharing between autonomous vehicles (AVs) and intelligent infrastructure, is considered a solution to many single-agent perception challenges. Early fusion, a data fusion scheme for the cooperative perception of AVs, provides a universally available data-sharing approach but has been criticized for its huge bandwidth consumption. This paper proposes a safety field (SF)-based vehicle-infrastructure cooperative perception approach by quantifying the driving risk in complex traffic scenarios. Leveraging the SF theory and point cloud downsampling, we design a delay-aware early fusion framework with adaptive communication volume control. We propose a latency-compensation error (LCE) for performance evaluation considering data transmission delay. The proposed framework is tested and verified in simulated city environments and simulated and real-world datasets. The experimental results show that the proposed approach increases the average precision (AP) and reduces the LCE compared with base models within a limited communication budget.

Parking scenarios present huge challenges for the prediction of intentions and situational awareness of autonomous vehicles. Unlike urban roads, parking scenarios feature greater freedom of movement, denser interactions, and more diverse intents. To address this issue, we propose the Parking Memory Interaction Transformer (PMI-Transformer) for predicting vehicle intent and trajectory based on cooperative vehicle-infrastructure systems (CVIS). Our model utilizes long-term semantic observation data and short-term historical trajectories to predict a target vehicle’s future intentions and trajectories from roadside perception units in CVIS. Based on global spatiotemporal features, a memory encoding strategy extracts essential information from the vehicle’s long-term history. At the same time, a hypergraph-based multiscale interaction module captures dense, diverse interactions in multi-vehicle environments. We incorporate parking occupancy data and calculate affinities between the vehicle and nearby spots to better characterize vehicle intent within the parking context. Experimental results show that our model outperforms state-of-the-art methods in intent and trajectory prediction, with visualizations confirming that PMI-Transformer effectively captures key historical information and social interactions in parking scenarios.

Pavement performance prediction is the foundation of maintenance decisions, which is the key problem of infrastructure management. Most prediction methods focus on section-based and annual deterioration on pavement, while it is hardly supporting daily and preventive maintenance plans. To fill in gaps in pavement forecasting for refined maintenance, this paper introduces a prediction model which is fine-grained on both temporal and spatial scales. Due to the coupling effects and action delay of multiple environmental factors, it is difficult to fathom and model the detailed deterioration process of pavement. Another problem is there are rare refined pavement datasets opening to public for research. Therefore, we establish a high-frequency and real-world pavement dataset and causal discovery is brought in to explicate the inner mechanism of the process. The proposed model first applies Partial Mutual Information from Mixed Embedding (PMIME) method for causal discovery, obtaining a causal graph and impact delays between factors and pavement performance. Based on this, we use an advanced pavement performance prediction model called Causal-Temporal Graph Convolution Network (CTGCN), combining the Graph Convolution Networks (GCNs) and the Long Short-Term Memory models (LSTMs) to capture causal features and temporal features simultaneously. We validate CTGCN model using collected datasets with two predictive time lengths. The experimental results prove that CTGCN model has better performance in both prediction accuracy and robustness than the state-of-art baseline. Dataset and more information are available at https://github.com/wowocai/CTGCN-dataset.

The contraflow left-turn (CLT) lane is a successful intersection design that can effectively increase the throughput of the left-turn traffic flow by allowing left-turn vehicles to use the exit lane. However, the noncooperative longitudinal and lane choice behavior of left-turn vehicles may underutilize the CLT lane. Further, left-turn traffic flow throughput changes with respect to the traffic situation and it will not achieve the maximum even if the left-turn traffic flow is split evenly among the lanes. To address these issues, we propose a cooperative longitudinal driving and lane assignment strategy that seeks to control the lane choices and longitudinal driving behavior of connected and autonomous vehicles (CAVs) at CLT intersections to minimize the total left-turn traffic delay. This approach establishes a mixed-integer linear model to optimize the longitudinal driving and lane choices for all left-turn vehicles simultaneously. It also takes into account the physical constraints of vehicles and the operational rules of the CLT intersection to ensure traffic safety and efficiency. To apply it in real time, a computationally efficient solution algorithm is developed based on the Lagrange relaxation method. It can solve the mixed-integer optimization problem within 0.02 seconds for vehicles less than 10. Numerical results demonstrate that the proposed cooperative control method outperforms the noncooperative-based control strategies developed upon the intelligent driver model (IDM) with a 24.66% reduction in average delay and a 20.44% increase in throughput. The proposed control strategy can enhance the left-turn traffic safety and efficiency for intersections with a CLT lane.

Sensors, such as cameras, millimeter-wave radar, and LiDAR, are widely deployed in cooperative vehicle-infrastructure systems. The demand for calibration of initial installation, damage replacements, and unstable installation has risen dramatically. Traditional methods require on-site operation and road closure; thus, repeated calibration can severely affect traffic conditions and expose operational personnel to potential safety threats. As more and more autonomous vehicles (AVs) flood the roads, this paper proposes an automatic calibration framework of roadside sensors by leveraging the high-precision positioning and perception data of AVs. First, we design a graph-based target-matching algorithm using an AV’s surrounding traffic perception data to identify the AV of interest from a dataset of multiple target trajectories recorded by roadside sensors. A line search algorithm is then designed to adjust the clock delay between sensors and establish the temporal correspondence, where a Gaussian process is applied to estimate the vehicle state in continuous time. Finally, we develop a least squares optimization model to complete the final calibration with the AV positioning data. The influence of measurement noise and missed detections on the proposed calibration framework are analyzed in simulated scenarios based on a Next Generation SIMulation (NGSIM) dataset, and the practicability is validated based on real-world data collected at Donghai Bridge, Hangzhou Bay Bridge, and DAIR-V2X dataset. It is shown that the proposed target matching algorithm can identify an AV trajectory from roadside sensor data with 20%-90% higher accuracy than baseline models, and the framework can accurately estimate the spatial and temporal parameters even with poor data quality. The mean least squares error of the trajectory alignment reaches centimeter-level accuracy.

Cameras, millimeter-wave radars, and lidars are widely deployed on smart roads to obtain personalized vehicle trajectories for advanced traffic control and risk avoidance. However, these asynchronous roadside sensors need to be spatiotemporally calibrated accurately before they are put into service. Traditional manual manipulation methods are inefficient and will affect traffic operation and safety. A rapid and convenient method has become essential under the trend that large amounts of roadside sensors need to be tested and calibrated frequently. As more and more connected and automated vehicles (CAVs) flood the smart roads, this paper proposes a novel spatiotemporal calibration framework using the positioning and perception data of CAVs. First, a trajectory matching algorithm is designed using motion feature and point feature histogram sequences as the descriptors, which can determine the approximate spatiotemporal correspondence for the CAV from the roadside trajectory dataset. An optimization method is then formulated to tune transformation parameters through the Gaussian Process trajectory representation and Gauss-Newton algorithms, considering the sampling frequency deviation and measurement noise. Based on numerical analysis via the NGSIM and HighD datasets, it is shown that the proposed calibration method can significantly reduce transformation errors and perform robustly in different scenarios. The feasibility and practicability of the calibration method are further validated through real-world experiments at Tongji University and on the Donghai Bridge in Shanghai, China. This study provides an economical and practical way for spatiotemporal calibration of roadside sensors in an era of CAVs.

Potential fields have been integrated with local path-planning algorithms for autonomous vehicles (AVs) to tackle challenging scenarios with dense and dynamic obstacles. Most existing potential fields are isotropic without considering the traffic agent’s geometric shape and could cause failures due to local minima. We propose a three-dimensional potential field (TriPField) model to overcome this drawback by integrating an ellipsoid potential field with a Gaussian velocity field (GVF). Specifically, we model the surrounding vehicles as ellipsoids in corresponding ellipsoidal coordinates, where the formulated Laplace equation is solved with boundary conditions. Meanwhile, we develop a nonparametric GVF to capture the multi-vehicle interactions and then plan the AV’s velocity profiles, reducing the path search space and improving computing efficiency. Finally, a local path-planning framework with our TriPField is developed by integrating model predictive control to consider the constraints of vehicle kinematics. Our proposed approach is verified in three typical scenarios, i.e., active lane change, on-ramp merging, and car following. Experimental results show that our TriPField-based planner obtains a shorter, smoother local path with a slight jerk during control, especially in the scenarios with dense traffic flow, compared with traditional potential field-based planners. Our proposed TriPField-based planner can perform emergent obstacle avoidance for AVs with a high success rate even when the surrounding vehicles behave abnormally.

With high-definition sensors and sophisticated machine vision algorithms, the visual perception capability of autonomous vehicle (AV) has largely advanced. However, the visual perception performance of AVs may still be unstable in complex traffic environment. To improve the robustness and capability of risk detection of AV visual perception system, this work proposes a framework to fuse human gaze and the object detection results from vehicle vision based on the Laplacian Pyramid algorithm. We evaluate the proposed method on a level-2 AV to perceive the interactive vehicles at unsignalized intersections. Using Extended Kalman Filter, the trajectory of the human drivers’ gaze and the anchor boxes from AV object detection are fused. Results reveal that with human-vehicle visual fusion, the actual trajectory of interactive vehicles can be predicted more accurately than separately using human gaze or object detection algorithm. The findings show that human-vehicle visual fusion improves the perception accuracy and robustness of interactive objects in complex traffic environment. The method has the potential to enhance the attention mechanism of AV vision.

Infrastructure-enabled autonomous driving systems have been increasingly applied in confined environments. Automated valet parking (AVP) in smart parking garages is one of the notable applications that require high-precision indoor positioning services. However, the performances of the existing wireless indoor positioning techniques, including Wi-Fi, Bluetooth, and ultra-wideband (UWB), tend to decline substantially as the working time increases and the building environment varies. In this paper, we propose a lifelong framework using crowdsourced data of fully instrumented autonomous vehicles(e.g. vehicles equipping with LiDAR), to maintain the availability and precision of indoor positioning systems. We establish a map-aided deep learning positioning correction model based on continuous data sequences, which utilizes convolution and long short-term memory (LSTM) modules to extract the spatial and temporal features of positioning errors. A local grid map generator is designed and embedded into the correction model to learn the influencing factors of errors from the building environment and facility. A deep-learning-based anomaly detector is designed to keep the lifelong stability of our framework. Based on the proposed method, we develop a lifelong UWB positioning correction system and apply it for the path tracking of AVP in a real underground parking garage. The test results show that the system can maintain positioning correction precision in the environment of varying sensor errors and reduce the positioning error by 60% and the tracking error by 40%. The study showcases an innovative infrastructure-enabled application that can accelerate the widespread use of autonomous driving systems.

Roadside sensors, such as camera and millimeter-wave (MMW) radar, provide traffic information beyond the visual range of intelligent vehicles in cooperative vehicle-infrastructure systems. Unlike onboard equipment, roadside sensors are affiliated with different systems and lack synchronization in both space and time. In this paper, we propose a novel spatio-temporal synchronization method of asynchronous roadside MMW radar–camera for sensor fusion, which utilizes features of the scenario to extract lane line corner points to pre-calibrate the camera. Based on the consistent time flow rate of the separate sensors, multiple virtual detection lines are set up to match the time headway of successive vehicles and conduct objective matching to track data. Finally, a synchronization optimization model is formulated and a constrained nonlinear minimization solver is applied to tune the parameters. Measure data from Donghai Bridge in Shanghai is applied to verify the feasibility and effectiveness of the method. The results determine that there are 33 frames (33*40 ms) of temporal deviation between the camera and the radar in this case. After the synchronization, the average spatial deviation is reduced from 2.47 m to 0.42 m in the X-direction and 64.06 m to 2.34 m in the Y-direction, respectively. This study provides an economical and effective way to solve the problem of spatio-temporal synchronization of roadside sensors.

Analysis of pavement deterioration is critical for road maintenance. Many section-based pavement performance evaluation methodologies have been investigated to determine the deteriorating tendency from a macro perspective. However, little research shed light on the refined deterioration analysis for single distress, which is valuable for daily and preventive maintenance. This paper proposed a deep-learning-based tracking framework to construct a large-scale continuous observation data set for every distress. A deep learning model is applied to detect six types of distress automatically. Then we adopted the spatial clustering method to match the pavement images in the same scene. Finally, image feature matching and perspective conversion methods are adopted to track the distress in the same scene. Using the data collected from the bus driving recorder, we have realized the daily observation of over 270 kilometers of the urban road network. More than 14,000 pavement distress have been continuously tracked, proving this framework’s effectiveness. In addition, the features of pavement deterioration are further discussed. The results show that heavy rain will significantly accelerate road surface deterioration. Under its influence, an intact pavement may suddenly deteriorate into serious potholes within a day. The established continuous pavement distress tracking dataset is significant for distress-level performance prediction research.

Serious Road anomalies caused by bridge approach settlement, pavement rutting, etc., not only seriously affect traffic safety and user experience but also aggravate the damage of road structure. It is more relevant to automated vehicles (AVs) as they are currently not designed to measure the pavement roughness directly. Without prior-collected road anomalies information, AVs’ active suspension control system can only passively reduce the negative impact of road anomalies to a certain extent. This paper proposed a response-type road anomaly detection and evaluation method by collecting the vibration data from AVs. A mechanical estimation model for the height of anomaly (HoA) is constructed to evaluate the degree of road anomaly. Passenger’s comfort is evaluated by three featured indicators: maximal acceleration, weighted root-mean-square acceleration, and jerk. A full-car simulation model is programmed based on the Simulink platform to reveal the relationship among road anomalies, comfort, and speed, which helps design a steady driving velocity profile for AVs. The results show that the root-mean-square error of road anomalies estimation is about 0.63cm. AVs’ comfort can be improved significantly by employing the proposed steady driving strategies.

Trajectory planning is essential for self-driving vehicles and has stringent requirements for accuracy and efficiency. The existing trajectory planning methods have limitations in the feasibility of planned trajectories and computational efficiency. This paper proposes a life-long learning framework to achieve effective and high-accuracy direct trajectory planning (DTP) tasks. Based on generative adversarial networks (GANs), this study develops a lightweight GDTP model to map the initial/final states and the control action sequence. Additionally, by embedding the GDTP into the rapidly-exploring random tree (RRT), a GDTP-RRT algorithm is further designed for long-distance and multi-stage planning tasks. Taking the tractor-trailer as an application case, we test the proposed method in multiple scenarios with varying characteristics. The experimental results show that the method can plan highly feasible trajectories in a short time, compared with the most applied algorithm – the cubic curve RRT* (CCRRT*). It is found that the tracking errors of our method are 29.1% and 44.1% lower than the CCRRT* in terms of position and heading angle. This paper provides an effective and stable vehicle trajectory planning method for complex self-driving tasks.

The failure to estimate friction on the road is a significant cause of roadway departure crashes. It is even more relevant to autonomous vehicles (AVs) as they are currently not designed to measure the skid resistance of roads. Most traditional measurements are too inefficient for real-time AV control. Thus, in this paper, we propose a dynamic method to estimate pavement friction level using computer vision. We collect 100 sets of high-quality road images and their resistance values to train the model, and design two major methods for analysis: 1) texture identification, consisting of grayscale-enhanced local binary pattern and the gray-level gradient co-occurrence matrix and 2) a deep neural network based on domain knowledge (TLDKNet). We introduce two standards of classification for model training and propose three indices-underestimation error, overestimation error, and accuracy-to verify the performance of our algorithms. The results showed the great correlation between pavement texture and skid resistance. The TLDKNet yielded the best performance with an accuracy of 90.67% and only 2.67% underestimation error, showing that it is sufficiently conservative with regard to safety. Based on the proposed method of estimation, a framework for anti-skid driving control is developed regarding the car-following behavior and turning movements. The anti-skid control framework provides new insights into enhancing the AV safety performance.

This paper develops a new approach for tram prioritization integrating an offline traffic signal timing planner with an online tram progression controller. The offline planner optimizes tram progressions by resynchronizing traffic signals to minimize tram running times, taking into account the effect of the resynchronization on other vehicles. The online controller aims at enhancing tram reliability by adopting three control strategies-green extension, vehicle holding, and speed guidance-to instruct the trams to travel within appropriate progressions. The real-world case studies are presented to demonstrate that, comparing with the state-of-the-practice approach, the proposed approach has the potential to improve the service quality by shortening tram running time and passenger waiting time, and to mitigate the negative impact on other vehicles by avoiding triggering unnecessary green extensions.