Ensuring the reliability of autonomous driving perception systems requires extensive environment-based testing, yet real-world execution is often impractical. Synthetic datasets have therefore emerged as a promising alternative, offering advantages such as cost-effectiveness, bias free labeling, and controllable scenarios. However, the domain gap between synthetic and real-world datasets remains a major obstacle to model generalization. To address this challenge from a data-centric perspective, this paper introduces a profile extraction and discovery framework for characterizing the style profiles underlying both synthetic and real image datasets. We propose Style Embedding Distribution Discrepancy (SEDD) as a novel evaluation metric. Our framework combines Gram matrix-based style extraction with metric learning optimized for intra-class compactness and inter-class separation to extract style embeddings. Furthermore, we establish a benchmark using publicly available datasets. Experiments are conducted on a variety of datasets and sim-to-real methods, and the results show that our method is capable of quantifying the synthetic-to-real gap. This work provides a standardized profiling-based quality control paradigm that enables systematic diagnosis and targeted enhancement of synthetic datasets, advancing future development of data-driven autonomous driving systems.

In contemporary society, autonomous driving systems face enormous challenges in various aspects, including the environment, traffic participants, and communication. The primary task of achieving vehicle autonomy is to ensure that Connected and Automated Vehicles (CAVs) can cooperate safely and efficiently with Human-Driven Vehicles (HDVs). Previous research mainly focused on strategies for purely CAV scenarios or treated HDVs as random factors in traffic, with relatively few studies considering the synchronous inducement and control of both types of vehicle within a unified framework. This research focuses on mixed traffic flow that includes both HDVs and CAVs, specifically addressing the merging problem at highway on-ramp entrances. We propose an inducement control method and a planning framework that target both types of vehicles simultaneously. For upstream HDVs, speed inducement is implemented based on time series predictions, along with behavior modeling that includes both internal and external uncertainties using a Gaussian mixture model. For ramp vehicles, merging decisions are made while considering the uncertainties of the upstream vehicles. Furthermore, based on the speed inducement strategy and the decision module, a cooperative uncertainty-aware planning model is constructed to achieve motion planning. The research demonstrates that the proposed speed inducement strategy can improve traffic safety and driver experience, and the cooperative control framework designed for both types of vehicles exhibits real-time functionality and performs excellently in terms of computation speed and planning success rates.

Agent faults pose a significant threat to the performance of multi-agent reinforcement learning (MARL) algorithms, introducing two key challenges. First, agents often struggle to extract critical information from the chaotic state space created by unexpected faults. Second, transitions recorded before and after faults in the replay buffer affect training unevenly, leading to a sample imbalance problem. To overcome these challenges, this paper enhances the fault tolerance of MARL by combining optimized model architecture with a tailored training data sampling strategy. Specifically, an attention mechanism is incorporated into the actor and critic networks to effectively and automatically detect fault information and dynamically regulate the attention given to faulty agents. Additionally, a prioritization mechanism is introduced to selectively sample transitions critical to current training needs. To further support research in this area, we design and open-source a highly decoupled code platform for fault-tolerant MARL, aimed at improving the efficiency of studying related problems. Experimental results demonstrate the effectiveness of our method in handling various types of faults, faults occurring in any agent, and faults arising at random times. Note to Practitioners—Multi-agent systems based on MARL outperform those using traditional control methods in terms of performance but remain highly vulnerable to unexpected faults. To improve fault tolerance in such systems, we introduce an attention mechanism that enables the neural network to dynamically adjust its focus on fault-related information. Additionally, a prioritization sampling strategy is employed to select critical samples from collected experiences that are most relevant to current training needs. Experimental results across various fault types demonstrate significant improvements in fault tolerance, validating the robustness of our approach. These findings suggest that the proposed method has the potential to be applied to real-world scenarios, such as multi-robot systems and autonomous vehicle fleets.

Testing and evaluation are critical to the development and deployment of autonomous vehicles (AVs). Given the rarity of safety-critical events such as crashes, millions of tests are typically needed to accurately assess AV safety performance. Although techniques like importance sampling can accelerate this process, it usually still requires too many tests for field testing. This severely hinders the testing and evaluation process, especially for third-party testers and governmental bodies with very limited testing budgets. The rapid development cycles of AV technology further exacerbate this challenge. To fill this research gap, this paper introduces the few-shot testing (FST) problem and proposes a methodological framework to tackle it. As the testing budget is very limited, usually smaller than 100, the FST method transforms the testing scenario generation problem from probabilistic sampling to deterministic optimization, reducing the uncertainty of testing results. To optimize the selection of testing scenarios, a cross-attention similarity mechanism is proposed to extract the information of AV’s testing scenario space. This allows iterative searches for scenarios with the smallest evaluation error, ensuring precise testing within budget constraints. Experimental results in cut-in scenarios demonstrate the effectiveness of the FST method, significantly enhancing accuracy and enabling efficient, precise AV testing.

The operational dynamics of vehicular transportation significantly influence energy expenditure and contribute to the escalation of global warming. However, a noticeable gap exists in the availability of Eco-driving methodologies tailored to mitigate conflicts between pedestrians and vehicles. In response, this study proposes a Vehicle-to-Pedestrian communication Eco-driving (V2P Eco-driving) strategy that operates without traffic lights and incorporates collaborative pedestrian trajectory prediction. Its performance is evaluated through a comparative study with the Ecological Intelligent Traffic Lights System (Eco-ITLS) strategy, which adjusts traffic light phases based on pedestrian and vehicle flow detection. To enhance the generalization of the prediction model, pedestrian social interactions are modeled using relative displacement and velocity metrics, while Kalman filtering mitigates systemic discrepancies in vehicular and infrastructural components. A modified distance-discrete dynamic programming (D-DDP) algorithm, accounting for remaining travel time, is introduced to optimize eco-friendly vehicle actions. The algorithm is benchmarked against other Eco-driving algorithms in terms of solution quality, memory consumption, and computational efficiency. Experimental results demonstrate that the proposed model achieves a balance between computational efficiency and solution quality. Real-world data validation and parameter calibration confirm its practicality. Simulations further highlight the V2P Eco-driving strategy’s significant potential for reducing energy consumption and emissions compared to conventional traffic light-based Eco-driving strategies.

Driving risk field (DRF) emerges as an effective way to assess the driving safety of connected and automated vehicles (CAVs). Most existing DRF models are established from the so-called birds-eye-view (BEV), which limits their accuracy for distributed vehicle-level tasks such as trajectory planning since the interactions between ego vehicle (EV) and its surrounding traffic environment have not been fully considered. To fill this research gap, we establish a novel DRF model from ego-vehicle-view (EVV) and apply it in trajectory planning in this paper. Firstly, the collision boundary between EV and its surrounding obstacles is defined by introducing the elliptical model to fully consider the geometry characteristics of vehicles. Secondly, the relative motion influence coefficient is designed to accurately characterize the relative motion between EV and obstacles, instead of using only basic driving state information such as location and velocity. On this basis, the unified DRF is established from EVV for driving safety assessment, which contains vehicle risk field (VRF) and lane marking risk field (LMRF). Based on the established DRF model, we then design a rolling trajectory planning method (RTPM) with a rolling horizon strategy, which not only ensures a long prediction horizon but also effectively reduces the computational complexity. Multiple simulation results under different traffic scenarios jointly verify the accuracy and applicability of the proposed RTPM and DRF model established from this new perspective.

The assessment of safety performance plays a pivotal role in the development and deployment of connected and automated vehicles (CAVs). A common approach involves designing testing scenarios based on prior knowledge of CAVs (e.g., surrogate models), conducting tests in these scenarios, and subsequently evaluating CAVs’ safety performances. However, substantial differences between CAVs and the prior knowledge can significantly diminish the evaluation efficiency. In response to this issue, existing studies predominantly concentrate on the adaptive design of testing scenarios during the CAV testing process. Yet, these methods have limitations in their applicability to high-dimensional scenarios. To overcome this challenge, we develop an adaptive testing environment that bolsters evaluation robustness by incorporating multiple surrogate models and optimizing the combination coefficients of these surrogate models to enhance evaluation efficiency. We formulate the optimization problem as a regression task utilizing quadratic programming. To efficiently obtain the regression target via reinforcement learning, we propose the dense reinforcement learning method and devise a new adaptive policy with high sample efficiency. Essentially, our approach centers on learning the values of critical scenes displaying substantial surrogate-to-real gaps. The effectiveness of our method is validated in high-dimensional overtaking scenarios, demonstrating that our approach achieves notable evaluation efficiency.

Testing and evaluating the safety performance of autonomous vehicles (AVs) is essential before the large-scale deployment. Practically, the number of testing scenarios permissible for a specific AV is severely limited by tight constraints on testing budgets and time. With the restrictions imposed by strictly restricted numbers of tests, existing testing methods often lead to significant uncertainty or difficulty to quantifying evaluation results. In this paper, we formulate this problem for the first time the "few-shot testing" (FST) problem and propose a systematic framework to address this challenge. To alleviate the considerable uncertainty inherent in a small testing scenario set, we frame the FST problem as an optimization problem and search for the testing scenario set based on neighborhood coverage and similarity. Specifically, under the guidance of better generalization ability of the testing scenario set on AVs, we dynamically adjust this set and the contribution of each testing scenario to the evaluation result based on coverage, leveraging the prior information of surrogate models (SMs). With certain hypotheses on SMs, a theoretical upper bound of evaluation error is established to verify the sufficiency of evaluation accuracy within the given limited number of tests. The experiment results on cut-in scenarios demonstrate a notable reduction in evaluation error and variance of our method compared to conventional testing methods, especially for situations with a strict limit on the number of scenarios.

Sampling, generation, and evaluation of scenarios are essential steps for intelligent testing of autonomous vehicles. Since uncertainty in driving behavior always leads to different occurrence frequencies of scenarios, we have to sample these scenarios in naturalistic datasets. Furthermore, a specified scenario needs to be further enriched and the driving behavior within it needs to be fully described to carry out generation in simulation systems. However, existing approaches generate scenarios randomly and uncontrollably, which makes them unable to precisely generate the specified scenarios. The driving behavior they describe is also memoryless and inflexible. To address the two issues, we propose a task-driven controllable scenario generation framework that can generate scenarios with the consideration of the driving behavior of Surrounding Vehicles (SVs) in a controllable manner. We first manually assign the driving behavior based on different testing tasks for all the considered vehicles. Then we expand the driving behavior temporally as the continuation and transition of several motion activities and generate the corresponding vehicle trajectories spatially. We adopt And-Or Graph (AOG) to model the transition between these motion activities. In contrast to the common memoryless Markov process, our framework generates driving behavior with continuity and driving memory. Finally, we evaluate our framework by generating lane-changing scenarios.

Safety performance evaluation is critical for developing and deploying connected and automated vehicles (CAVs). One prevailing way is to design testing scenarios using prior knowledge of CAVs, test CAVs in these scenarios, and then evaluate their safety performances. However, significant differences between CAVs and prior knowledge could severely reduce the evaluation efficiency. Towards addressing this issue, most existing studies focus on the adaptive design of testing scenarios during the CAV testing process, but so far they cannot be applied to high-dimensional scenarios. In this paper, we focus on the adaptive safety performance evaluation by leveraging the testing results, after the CAV testing process. It can significantly improve the evaluation efficiency and be applied to high-dimensional scenarios. Specifically, instead of directly evaluating the unknown quantity (e.g., crash rates) of CAV safety performances, we evaluate the differences between the unknown quantity and known quantity (i.e., control variates). By leveraging the testing results, the control variates could be well-designed and optimized such that the differences are close to zero, so the evaluation variance could be dramatically reduced for different CAVs. To handle the high-dimensional scenarios, we propose the sparse control variates method, where the control variates are designed only for the sparse and critical variables of scenarios. According to the number of critical variables in each scenario, the control variates are stratified into strata and optimized within each stratum using multiple linear regression techniques. We justify the proposed method’s effectiveness by rigorous theoretical analysis and empirical study of high-dimensional overtaking scenarios.

Autonomous driving and its widespread adoption have long held tremendous promise. Nevertheless, without a trustworthy and thorough testing procedure, not only does the industry struggle to mass-produce autonomous vehicles (AV), but neither the general public nor policymakers are convinced to accept the innovations. Generating safety-critical scenarios that present significant challenges to AV is an essential first step in testing. Real-world datasets include naturalistic but overly safe driving behaviors, whereas simulation would allow for unrestricted exploration of diverse and aggressive traffic scenarios. Conversely, higher-dimensional searching space in simulation disables efficient scenario generation without real-world data distribution as implicit constraints. In order to marry the benefits of both, it seems appealing to learn to generate scenarios from both offline real-world and online simulation data simultaneously. Therefore, we tailor a Reversely Regularized Hybrid Offline-and-Online ((Re)2H2O) Reinforcement Learning recipe to additionally penalize Q-values on real-world data and reward Q-values on simulated data, which ensures the generated scenarios are both varied and adversarial. Through extensive experiments, our solution proves to produce more risky scenarios than competitive baselines and it can generalize to work with various autonomous driving models. In addition, these generated scenarios are also corroborated to be capable of fine-tuning AV performance.

A brain–computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, and so on. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence, privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This article fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs.

Electric buses (e-buses) are increasingly adopted in the transit systems for their benefits of reduced roadside pollution and better onboard experience. E-bus scheduling is a critical problem in the operation planning stage of transit management to ensure efficient and reliable transit service. In Chinese mega cities, the e-bus networks are operated with many bus routes characterized by high service frequency and long operation time, making the e-bus scheduling a large-scale problem. Besides, the transit agency requires the vehicle-depot constraint to limit the e-bus reposition since it is not cost-efficient. An efficient method to generate optimized schedules that meet the above requirements is desired by the transit agencies. In this paper, we address a large-scale multi-depot electric bus scheduling problem considering the vehicle-depot constraint and partial recharging policy. A mixed integer programming model and an efficient branch-and-price (BP) algorithm are developed to solve the problem. In the BP algorithm, we devise a heuristic method to generate good initial solutions and adopted heuristic decisions in the label setting algorithm to solve the pricing problem. In this way, the efficiency of the BP algorithm is achieved and the large-sized problem instances can be solved. We conduct extensive numerical experiments based on the fixed-route and multi-route operation cases in Shenzhen. The results show that the BP algorithm can generate provable high-quality solutions. Sensitivity analysis indicates that increasing battery capacity and charging rate can reduce the operational cost. The optimal charging schedules can also provide guide in determining the capacity of the charging facilities.

Owing to the development of fast-charging technologies, the problems of limited driving range and long charging time for battery electric buses (BEBs) can be alleviated. However, fast charging may lead to a high energy cost because of charging during periods of high electricity rates and a high-power peak and consequently enhanced electricity demand cost. The reason is that the bus service and charging schedules can both affect the charging behavior and there is strong interaction between them. However, there is lack of researches to consider these two aspects jointly to optimise the charging behavior of large-scale BEB network. In this study, a BEB network charging optimisation (BEB–NCO) model is proposed to cooperatively optimise the bus service and charging schedules to minimise the charging cost for a fast-charging BEB network. To enhance the computing efficiency for large-scale networks, a heuristic algorithm, an integration of adaptive large neighbourhood searching and branch & bound (ALNS–BB), has been developed. The developed model and the heuristic algorithm were applied to a real-world BEB network in Shenzhen, China. The results show that the ALNS–BB algorithm can reduce the computational time by at least 82.56% and the bus service and charging schedule generated by BEB–NCO model can reduce the charging cost by up to 53.35% compared with the existing strategy. Furthermore, sensitivity analyses were conducted to investigate the impact of the charging power on the charging cost. It is suggested that a charging power of 115 kW is recommended in this case.

Building energy consumption accounts for a large figure of total energy consumption and keeps a rapid increase. Energy for heating, ventilation, and air conditioning (HVAC) is the main contribution. To save energy with maintaining comfort, control methods have been studied, including rule-based methods, model predictive control, and deep reinforcement learning (DRL). While their performance in real applications can be restricted by the highly nonstationary building environment caused by factors like weather conditions. Especially, for multizone HVAC control with multiple controllers, variation of the controller policy causes potential nonstationarity for each other. Current solutions to the nonstationarity based on model-based methods add complexity for building modeling and decrease the control efficiency. In addition, although massive data are available with the development of the Internet of Things (IoT) in smart buildings, high-level exploitation of data in the context-aware system is not yet explored to detect environment changes for smart building control. To this end, we propose a novel context-aware model-free DRL method called Trans-Context soft actor–critic (SAC) for multizone HVAC control, which combines a transformer-encoder-based context-aware system and the state-of-the-art DRL algorithm SAC. The context-aware system disentangles the nonstationarity by learning context data from IoT sensors. Besides, Trans-Context SAC is a model-free method without the need for building modeling. We evaluate Trans-Context SAC in a simulation-based case study on a multizone commercial building. Results demonstrate that Trans-Context SAC can achieve up to 15.9% of energy saving compared to other baselines with maintaining thermal comfort. Besides, Trans-Context SAC obtains the generalization for unseen environments.

How to generate testing scenario libraries for connected and automated vehicles (CAVs) is a major challenge faced by the industry. In previous studies, to evaluate maneuver challenge of a scenario, surrogate models (SMs) are often used without explicit knowledge of the CAV under test. However, performance dissimilarities between the SM and the CAV under test usually exist, and it can lead to the generation of suboptimal scenario libraries. In this article, an adaptive testing scenario library generation (ATSLG) method is proposed to solve this problem. A customized testing scenario library for a specific CAV model is generated through an adaptive process. To compensate for the performance dissimilarities and leverage each test of the CAV, Bayesian optimization techniques are applied with classification-based Gaussian Process Regression and a newly designed acquisition function. Comparing with a pre-determined library, a CAV can be tested and evaluated in a more efficient manner with the customized library. To validate the proposed method, a cut-in case study is investigated and the results demonstrate that the proposed method can further accelerate the evaluation process by a few orders of magnitude.

Cooperative driving at signal-free intersections, which aims to improve driving safety and efficiency for connected and automated vehicles, has attracted increasing interest in recent years. However, existing cooperative driving strategies either suffer from computational complexity or cannot guarantee global optimality. To fill this research gap, this paper proposes an optimal and computationally efficient cooperative driving strategy with the polynomial-time complexity. By modeling the conflict relations among the vehicles, the solution space of the cooperative driving problem is completely represented by a newly designed small-size state space. Then, based on dynamic programming, the globally optimal solution can be searched inside the state space efficiently. It is proved that the proposed strategy can reduce the time complexity of computation from exponential to a small-degree polynomial. Simulation results further demonstrate that the proposed strategy can obtain the globally optimal solution within a limited computation time under various traffic demand settings.

The properties of cooperative driving strategies for planning and controlling Connected and Automated Vehicles (CAVs) at intersections range from some that achieve highly efficient coordination performance to others whose implementation is computationally fast. This paper comprehensively compares the performance of four representative strategies in terms of travel time, energy consumption, computation time, and fairness under different conditions, including the geometric configuration of intersections, asymmetry in traffic arrival rates, and the relative magnitude of these rates. Our simulation-based study has led to the following conclusions: 1) The Monte Carlo Tree Search (MCTS)-based strategy achieves the best traffic efficiency and has great performance in fuel consumption; 2) MCTS and Dynamic Resequencing (DR) strategies both perform well in all metrics of interest. If the computation budget is adequate, the MCTS strategy is recommended; otherwise, the DR strategy is preferable; 3) An asymmetric intersection has a noticeable impact on the strategies, whereas the influence of the arrival rates can be neglected. When the geometric shape is asymmetrical, the modified First-In-First-Out (FIFO) strategy significantly outperforms the FIFO strategy and works well when the traffic demand is moderate, but their performances are similar in other situations; and 4) Improving traffic efficiency sometimes comes at the cost of fairness, but the DR and MCTS strategies can be adjusted to realize a better trade-off between various performance metrics by appropriately designing their objective functions.

We address the problem of optimally controlling Connected and Automated Vehicles (CAVs) arriving from four multi-lane roads at a signal-free intersection where they conflict in terms of safely crossing (including turns) with no collision. The objective is to jointly minimize the travel time and energy consumption of each CAV while ensuring safety. This problem was solved in prior work for single-lane roads. A direct extension to multiple lanes on each road is limited by the computational complexity required to obtain an explicit optimal control solution. Instead, we propose a general framework that first converts a multi-lane intersection problem into a decentralized optimal control problem for each CAV with less conservative safety constraints than prior work. We then employ a method combining optimal control and control barrier functions, which has been shown to efficiently track tractable unconstrained optimal CAV trajectories while also guaranteeing the satisfaction of all constraints. Simulation examples are included to show the effectiveness of the proposed framework under symmetric and asymmetric intersection geometries and different CAV sequencing policies.

Testing scenario library generation (TSLG) is a critical step for the development and deployment of connected and automated vehicles (CAVs). In Part I of this study, a general method for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evaluation. This paper aims to provide implementation examples and guidelines, and to enhance the proposed methodology under high-dimensional scenarios. Three typical cases, including cut-in, highway-exit, and car-following, are designed and studied in this paper. For each case, the process of library generation and CAV evaluation is elaborated. To address the challenges brought by high dimensionality, the proposed method is further enhanced by reinforcement learning technique. For all three cases, results show that the proposed method can accelerate the CAV evaluation process by multiple magnitudes with same evaluation accuracy, if compared with the on-road test method.

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and yet there is no systematic framework to generate testing scenario library. This study aims to provide a general framework for the testing scenario library generation (TSLG) problem with different operational design domains (ODDs), CAV models, and performance metrics. Given an ODD, the testing scenario library is defined as a critical set of scenarios that can be used for CAV test. Each testing scenario is evaluated by a newly proposed measure, scenario criticality, which can be computed as a combination of maneuver challenge and exposure frequency. To search for critical scenarios, an auxiliary objective function is designed, and a multi-start optimization method along with seed-filling is applied. Theoretical analysis suggests that the proposed framework can obtain accurate evaluation results with much fewer number of tests, if compared with the on-road test method. In part II of the study, three case studies are investigated to demonstrate the proposed method. Reinforcement learning based technique is applied to enhance the searching method under high-dimensional scenarios.

Connected and automated vehicles (CAVs) show great potential to improve both traffic efficiency and safety by sharing information. This paper addresses the problem of coordinating two strings of vehicles at highway on-ramps efficiently and safely in the longitudinal direction. A rule-based adjusting algorithm is proposed to achieve a near-optimal merging sequence for vehicles coming from the mainline and entering through the ramp. Optimality analysis indicates that the proposed method performs very well compared with the global optimal solutions. Furthermore, to investigate the effectiveness and robustness of the proposed method, simulation-based case studies are carried out under both balanced and unbalanced scenarios. The results are compared with two other control strategies (i.e., rule-based methods and optimization-based methods) in terms of throughput, delay, computational cost, and fuel consumption.

In this paper, we propose a new cooperative driving strategy for connected and automated vehicles (CAVs) at unsignalized intersections. Based on the tree representation of the solution space for the passing order, we combine Monte Carlo tree search (MCTS) and some heuristic rules to find a nearly global-optimal passing order (leaf node) within a very short planning time. Testing results show that this new strategy can keep a good tradeoff between performance and computation flexibility.

Driving at velocity much lower than the speed limit greatly restraints the movements of the followers, resulting in considerable waste of road resource every day. When two slow vehicles are moving abreast on the city road, the result is devastating for their followers. To solve such problem by the idea the i-Vics (intelligent vehicular infrastructure cooperative systems), this paper proposes a dedicated on-road traffic management plan for abreast low speed connected vehicles, especially for scenarios near signalized intersections. The management plan takes three steps to erase the traffic blockage caused by those abreast slow drivers, including detection of abreast slow drivers, appraisal of environmental factors, and traffic guidance notification for all relevant drivers. In the proposed plan, the detection model initially evaluates the whole street and picks out all the abreast low speed vehicles based on the i-Vics. For every blockage target, a appraisal model of the surroundings determines the strategy how to dissolve the traffic blockage, including the feasibility of overtaking, and possibility of passing through the intersection and etc. Finaly, once the dissolving strategy is approved by the appraisal program, the management system sends notification of speed guidance to all relevant drivers to guide them to pass through the signalized intersection within the remaining green time.

The application of modern technologies makes it possible for a transportation system to collect real-time data of some specific traffic scenes, helping traffic control center to improve the traffic efficiency. Based on such consideration, we introduce a variant deep reinforcement learning agent that might take advantage of the real-time GPS data and learn how to control the traffic lights in an isolated intersection. We combine the recurrent neural network (RNN) with Deep Q-Network, namely DRQN and compare its performance with standard Deep Q-Network (DQN) in partially observed traffic situations. The agent is trained by using Q-learning with experience replay in traffic simulator SUMO, so as to generate traffic signal control policy. Based on the experiments, both DQN and DRQN method are able to adjust its traffic signal timing policy to specific traffic environment and achieve lower average vehicle delay than fixed time control. In addition, the recurrent Q-learning method gets better simulation result than standard Q-learning method in the environment of different probe vehicle proportion.

With the rapid development in vehicular communication technologies, cooperative driving of intelligent vehicles can provide promising efficiency, safety and sustainability to the intelligent transportation systems. In this paper, a centralized cooperative intersection control (CCIC) approach is proposed for the non-signalized intersections under automated vehicle environment. The cooperative intersection control problem is converted to a nonlinear constrained programming problem considering vehicle delay, fuel consumption, emission and driver comfort level. Furthermore, a simulation-based case study is carried out on a four-legged, two-lane non-signalized intersection under different traffic volume scenarios to compare CCIC with the actuated intersection control (AIC) system. The results indicate that the CCIC approach shows significant potential improvements on the traffic efficiency (i.e., nearly 14% of traffic flow increase, nearly 90% of travelling time saving), emission (nearly 60% of CO2reduction) and driver comfort level (nearly 2% of comfort level increase).

With the advance of intelligent vehicle infrastructure cooperation systems (i-VICS), many traffic parameters can be inferred given data from the OBU (On Board Unit of i-VICS)-equipped vehicles to calculate some information used in the modern urban traffic management. As a typical application, the estimation of investigated queue length at isolated intersections is proposed in the paper. Firstly, the queue length estimation at isolated intersections is explored to be transformed into the problem of deriving the number of queued vehicles. Two models, the improved and advanced interpolation methods, are then introduced to the condition of single cycle and multiple cycles, respectively. The microscopic simulation software VISSIM is adopted to evaluate the effect of the derived models. Its analysis shows that, under the condition of single cycle, the mean absolute error (MAE) of the improved interpolation method is less than 2 vehicles when the equipped rate of i-VICS OBU is higher than 50% and the maximum MAE with 30% equipped rate will be no more than 4 vehicles. On the other hand, under the condition of multiple cycles, the MAE of the advanced interpolation method with very low OBU-equipped rates will be less than 6 vehicles. Finally, numerical results of proposed approach have shown the relations of MAE with the volume-to-capacity ratio and the OBU-equipped rate.

Safety, mobility and environmental sustainability represent three cornerstones when evaluating the effectiveness of an intelligent transportation system. However, very few studies have conducted a holistic performance assessment for a connected vehicle (CV) based application. In this study, an environment-focused CV application, called Eco-Approach and Departure (EAD) application at signalized intersections, is used as an example. Its safety, mobility and environmental sustainability parameters are carefully evaluated through comprehensive simulation analyses over a real-word network. A variety of scenarios have been tested and the impact analysis is conducted from two perspectives: 1) EAD-equipped vehicles vs. non-equipped vehicles; and 2) overall traffic. The results indicate that the benefits of mobility and environmental sustainability show more consistent patterns across different scenario while safety impacts are more scenario-dependent.

This paper discusses the trend modeling for traffic time series. First, we recount two types of definitions for a long-term trend that appeared in previous studies and illustrate their intrinsic differences. We show that, by assuming an implicit temporal connection among the time series observed at different days/locations, the PCA trend brings several advantages to traffic time series analysis. We also describe and define the so-called short-term trend that cannot be characterized by existing definitions. Second, we sequentially review the role that trend modeling plays in four major problems in traffic time series analysis: abnormal data detection, data compression, missing data imputation, and traffic prediction. The relations between these problems are revealed, and the benefit of detrending is explained. For the first three problems, we summarize our findings in the last ten years and try to provide an integrated framework for future study. For traffic prediction problem, we present a new explanation on why prediction accuracy can be improved at data points representing the short-term trends if the traffic information from multiple sensors can be appropriately used. This finding indicates that the trend modeling is not only a technique to specify the temporal pattern but is also related to the spatial relation of traffic time series.

The classical mean shift algorithm is easy to pass into local maxima, which is caused by the lack of appropriate target model updating mechanism. In this paper, a SIFT-based mean shift algorithm is proposed, which can be used for continuous vehicle tracking in complex situations, such as the shape and the illumination of the vehicle object change. In our algorithm, the mean shift algorithm is utilized to determine the candidate target region, and then a judgment on the tracking effect is made according to the Bhattacharyya coefficient. If tracking fails, the candidate area is matched with the target model by SIFT feature, and a new track position is determined. Otherwise, the target model is periodically updated by SIFT feature matching, and the target model can be constantly updated according to the state change of the moving vehicle. In the scenes of moving vehicle target deformations, such as the variation of scale and illumination, the algorithm is tested and compared with other algorithms. The experimental results show that the proposed method can effectively track an object under the condition of varying illumination and shape deformation.

With the dramatic development of image processing technology, a growing number of traffic flow detection and analyses have been conducted by using video data. Time to collision (TTC) and postencroachment time (PET) are two major parameters used to indicate the severity of a potential collision and to capture an imminent vehicular accident. However, microlevel pedestrian-involved collisions are less studied because they are hard to observe or record. This paper tries to extract the traffic object locations from video data, to define the time difference to collision (TDTC) parameter as a variation from TTC and PET to fit the pedestrian-involved potential collisions/conflicts, analyze the interaction behavior between pedestrian and vehicles, and validate the TDTC parameter in indicating pedestrian safety performance by using 100 groups of interaction data. The results show that the interaction cases with larger TDTC values are safer, whereas the cases with continuously closer to zero TDTC values are more dangerous. About 80% of the cases classified by the TDTC parameter have the same result with the independent observation; if TDTC is combined with vehicle speed, the classification result can be improved. More mixed traffic scenes will be conducted based on this research in the future.

Short-time traffic flow prediction is a significant interest in transportation study, and it is essential in congestion control and traffic network management. In this paper, we propose an Autoregressive Integrated Moving Average with Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GARCH) model for traffic flow prediction. The model combines linear ARIMA model with nonlinear GARCH model, so it can capture both the conditional mean and conditional heteroscedasticity of traffic flow series. The model is calibrated, validated and used for prediction based on PeMS single loop detector data. The performance of the hybrid model is compared with that of standard ARIMA model. The results show that the introduction of conditional heteroscedasticity cannot bring satisfactory improvement to prediction accuracy, in some cases the general GARCH(1,1) model may even deteriorate the performance. Thus for ordinary traffic flow prediction, the standard ARIMA model is sufficient.

In this paper, we link two research directions of road traffic-the mesoscopic headway distribution model and the microscopic vehicle interaction model-together to account for the empirical headway/spacing distributions. A unified car-following model is proposed to simulate different driving scenarios, including traffic on highways and at intersections. Unlike our previous approaches, the parameters of this model are directly estimated from the Next Generation Simulation (NGSIM) Trajectory Data. In this model, empirical headway/spacing distributions are viewed as the outcomes of stochastic car-following behaviors and the reflections of the unconscious and inaccurate perceptions of space and/or time intervals that people may have. This explanation can be viewed as a natural extension of the well-known psychological car-following model (the action point model). Furthermore, the fast simulation speed of this model will benefit transportation planning and surrogate testing of traffic signals.

In this letter, we show that under the assumption of high resolution, the quantization errors of fGn and fBm signals with uniform quantizer can be treated as uncorrelated white noises.

The missing data problem greatly affects traffic analysis. In this paper, we put forward a new reliable method called probabilistic principal component analysis (PPCA) to impute the missing flow volume data based on historical data mining. First, we review the current missing data-imputation method and why it may fail to yield acceptable results in many traffic flow applications. Second, we examine the statistical properties of traffic flow volume time series. We show that the fluctuations of traffic flow are Gaussian type and that principal component analysis (PCA) can be used to retrieve the features of traffic flow. Third, we discuss how to use a robust PCA to filter out the abnormal traffic flow data that disturb the imputation process. Finally, we recall the theories of PPCA/Bayesian PCA-based imputation algorithms and compare their performance with some conventional methods, including the nearest/mean historical imputation methods and the local interpolation/regression methods. The experiments prove that the PPCA method provides significantly better performance than the conventional methods, reducing the root-mean-square imputation error by at least 25%.

With the advent and applications of sensor networks, pervasive traffic information can be gathered rapidly and collaboratively, which can upgrade traditional traffic monitoring system and enable many unprecedented services for travelers. In this paper, three-tiered sensor network architecture is proposed to approach the novel traffic information service system that would dramatically improve the ways and qualities of traffic information collection. A brief analysis of the important requirements of system, as well as the key issues that are faced in the design process, is described. The detailed strategies at each level, from the network architecture, to the sensor unit configuration and software deployment, to the operation of system, are explained. The implementation of a pilot platform is discussed in the end.

The spatial-temporal traffic data analysis based on global data management is a newly developed and crucial approach to help traffic managers having the global view of urban traffic status in the level of road network, which is very clearly useful in traffic control and route guidance. The multiagent systems are used in traffic data management with full consideration of the characteristics of traffic data and the cooperation and workflow among them. In software implementation of data management, the agent-based common object request broker architecture is adopted taking the distributed urban traffic data in the large area under network environments into account. Based on the global traffic data, the approach of visualized spatial-temporal analysis is then induced. The similarity of traffic data is analyzed first for each link and its profile is achieved to undertake the primary processing of urban traffic data. Furthermore, analysis results are shown on the basis of the geographic information systems for transportation. The two types of visualization, pseudocolor and contour maps, are adopted in the demonstration to display the traffic status graphically and its changing frames. Among the applications in some big cities in China, the case of urban traffic analysis for Beijing is studied to demonstrate the implementation of the approach.

This paper introduces a novel networked traffic parameter forecasting method. Based on the detailed analysis of the literature, the paper describes the fundamental ideas. Then we select a typical traffic network in Beijing City. In order to simplify the problem, we classify the links using clustering analysis and find the representative links in each group. Furthermore, we introduce the Markov chain model to predict the traffic parameter. EM algorithm is applied to estimate the parameters of mixed Gaussian distributions, i.e., means, covariances and mixing coefficients. According to the regression equations between the representative links and the other links in the same group, we can obtain all the predicted traffic parameters of all the link in the road network. The case studies using real data from UTC-SCOOT system in Beijing have proved the effectiveness and applicability of the proposed method.

In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) for an isolated four approaches intersection with through and left-turning movements is presented. This method has the adaptive signal tinting ability, and can make adjustments to signal tinting in response to observed changes. The "urgency degree" term, which can describe the different user's demand for green time is used in decision-making by which strategy of signal tinting can be determined. Using fuzzy logic controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. In this paper, a method based on fuzzy-neuro can be used to predict traffic parameters used in fuzzy logic controller. Simulation results show that the our proposed has the ability to adjust its signal timing in response to changing traffic conditions on a real-time basis, and our proposed controller produces lower vehicle delays and percentage of stopped vehicles than the traffic actuated controller.

This paper presents a fuzzy logic adaptive traffic signal controller for an isolated four-approaches intersection with through and lest-turning movements. The controller has the ability to make adjustments to signal timing in response to observed changes. The "urgency degree" term, which can describe the different user's demand for green time is used in fuzzy logic decision-making in our algorithm. Using three levels model of fuzzy controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. This paper describes a flexible form of fuzzy logic signal control whose performance can be tuned off-line using a set of parameters which define the fuzzy set membership functions for the input variables. Our work also demonstrates the feasibility of using a multiobjective genetic algorithm (MOGA) to find a set of optimal parameters for a fuzzy controller with a range of possibly conflicting performance measures. Simulation results show that our proposed controller produces lower vehicle delays and percentage of stopped vehicles than the traffic-actuated controller.