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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (T-ITS) has become a leading international journal in the field of ITS since its first issue published in 2000. To obtain a structural overview as well as the evolution of T-ITS in the last decade (2010–2019), we present a comprehensive bibliometric analysis from multiple perspectives based on the articles published during the same period in this paper. Our analyses of the T-ITS literature include: (a) statistical analysis;(b) topic analysis; and (c) network analysis. Statistical analysis was first conducted to identify the most highly cited papers, then the top productive and influential authors, institutions and countries/regions were given from paper counts and citations respectively. In order to identify important topics and patterns of evolution, author keywords were used to identify the most frequent topics and the theme river map can visually demonstrate their corresponding trends. Furthermore, keyword co-occurrence network was constructed to reveal the hotspots and the research landscape within this field. In addition, three networks are provided to visualize the relationships and reveal the collaboration patterns from different perspectives, including authors, institutions and countries/regions. The results provide an insight on the characteristics of the publications over the last decade, from which we can know the key contributors and groups who brought the significant growth of the journal. This paper can benefit researchers in terms of promoting understanding of the entire field with the development status and trends. [ABSTRACT FROM AUTHOR]

The widespread communication of academic ideas and research achievements through literature and media has generated massive academic big data. Analyzing such academic big data and discovering knowledge can discover comprehensive and predictive academic intelligence and provide corresponding services, which are valuable for scholars, journals, institutions and governments for career assessment, topic selection, funding management and resource allocation. This paper proposes a heterogeneous knowledge-learning method for understanding and predicting academic impact in the transportation field. The proposed method is illustrated on the academic big data collected from the papers published in 34 transportation journals from 2008 to 2018. We extract four types of features including bibliometric, altmetric, network and semantic features, and build hybrid feature embedding via TransR and Doc2vec that involving domain knowledge. Further, an academic impact prediction model for articles named as Hy-LSTM-Att is proposed, which weighs the hybrid features by the attention mechanism and predicts academic impact with the bi-LSTM recurrent neural networks. Experimental results demonstrate that the proposed Hy-LSTM-Att model outperforms competing shallow and deep learning models. Additionally, the feature ablation experiments illustrate that the four types of features positively contribute to the performance of impact prediction. [ABSTRACT FROM AUTHOR]

In the context of current smart cities, Cooperative Intelligent Transportation Systems (C-ITS) represent one of the main use case scenarios that aim to improve peoples’ daily lives. Thus, during the last few years, numerous standards have been adopted to regulate such networks. Within a C-ITS, a large number of messages are exchanged continuously in order to ensure that the different applications operate efficiently. However, these networks can be the target of numerous attacks. The sybil attack is among the most dangerous ones. In a sybil attack, an attacker creates multiple identities and then disguises as several fake stations in order to interfere with the normal operations of the system or profit from provided services. We analyze recently proposed sybil detection approaches regarding their compliance with the current C-ITS standards as well as their evaluation methods. We provide several recommendations such as network and attack models as well as an urban and highway datasets that can be considered in future research in sybil attack detection. [ABSTRACT FROM AUTHOR]

The IEEE Transactions on Intelligent Transport Systems was founded in 2000 to enhance the sharing of international research on theoretical and practical technology developments in the ITS field. Since then, it has become a leading journal in the field and has attracted a high caliber of multi-disciplinary authors and publications. In recognition of twenty plus years of contributions to the field, this paper analyses the evolution of the journal over its lifetime for the period 2000–2021. A bibliometric analysis is conducted on 3,428 peer-reviewed publications (articles and reviews) using data collected from Core Collection Database of Web of Science. The paper identifies the most influential and cited articles and their impacts on the development of research in the ITS field. The analysis also includes detailed information on top leading authors, their organizations and countries where the research was funded and developed. The analysis shows how the growing interest and diversity of transport technology topics has led to an increase in the number and quality of publications in journal over the past twenty plus years. A visualization of bibliographic coupling, co-authorship and keywords analysis is also presented using the VOSviewer software leading to insightful findings regarding the journal’s impact and standing in this field of research. [ABSTRACT FROM AUTHOR]

Distracted or drowsy driving is unsafe driving behavior responsible for thousands of crashes every year. Studying driver behavior has challenges associated with observing drivers in their natural environment. The naturalistic driving study (NDS) has become the most sought-after approach, since it eliminates the bias of a controlled setup, allowing researchers to understand drivers’ behavior in real-world scenarios. Video recordings collected in NDS research are incredibly insightful in identifying driver errors. Computer vision techniques have been used to autonomously analyze video data and classify drivers’ behavior. While computer vision scientists focus on image analytics, NDS researchers are interested in the factors impacting driver behavior. This survey paper makes a concerted effort to serve both communities by comprehensively reviewing studies, describing their data collection, computer vision techniques implemented, and performance in classifying driver behavior. The scope is limited to studies employing at least one camera observing the driver inside a vehicle. Based on their objective, papers have been classified as detecting low-level (e.g. head orientation) or high-level (e.g. distraction detection) driver information. Papers have been further classified based on the datasets they employ. In addition to twelve public datasets, many private datasets have also been identified, and their data collection design is discussed to highlight any impact on model performance. Across each task, algorithms employed and their performance are discussed to establish a baseline. A comparison of different frameworks for NDS video data analytics throws light on the existing gaps in the state-of-the-art that can be addressed by future computer vision research. [ABSTRACT FROM AUTHOR]

Public Bike Systems (PBSs) offer the popular service for the short distance in daily life. The battery powered bike is an interesting and feasible method to extend the bike trip length, which can promote the PBS service but faces the challenges caused by the limited budget for the battery cabinet deployment and user demand. Thus, the realistic problem is how to deploy the cabinets near a part of public bike stations by considering the challenges. This paper is the first to study the novel problem, Cabinet Deployment Problem (CDP) in PBS, based on the features extracted from the real dataset of PBS in Hangzhou China, and proposes our strategies in the case of the Euclidean space and Manhattan model. In the Euclidean space, CDP can be specified as the ${e}$ lectric-bike Set Cover problem (e-SC), and this paper proposes a Greedy Station Coverage algorithm (GSC). Its distributed version, called the Localized Greedy Selection algorithm (LGS), is also presented because of the large amount of bike stations. In many cities, the roads have Manhattan-type directions, i.e., either east-west or south-north. In order to close to the realistic scenario, this paper develops a Genetic Algorithm based Cabinet Search algorithm (GAS) to determine the locations for the cabinet deployment in the Manhattan model. The extensive numerical experiment is conducted for our strategies, which are compared to a straightforward method, the Random Placement Strategy (RPS) under the diverse parameter settings. [ABSTRACT FROM AUTHOR]

Recognition of aggressive driving behavior helps future research and practices in Intelligent Transport Systems. This paper tries to briefly explain the concepts related to aggressive driving behavior and introduces a system integrated with a novel machine learning algorithm for the recognition of trip-based aggressive driving behavior. The algorithm is a Gaussian Mixture Model (GMM) structured with Factor Analysis (FA), and Hierarchical Clustering (HC): common factors were extracted using FA, which is further applied to HC and GMM in the recognition of trip-based aggressive driving. The system is applied in a case study using data from the Shanghai Naturalistic Driving Study, for simulating data collection using the Advanced Driving Assistance System (ADAS) system in a real-traffic situation. Three behavior types (cautious, regular, and aggressive driving) were successfully clustered. For validity, the real aggressive driving behavior records were extracted based on the video, and the proposed system was compared with existing recognition methods. Results indicate that the accuracy of aggressive driving recognition of the system is higher than others (accuracy = 87%). This paper provides a reference in defining and determining aggressive driving, and a robust system for aggressive driving behavior recognition along with the trained algorithm, which can be used in real-world applications for improving driving safety with the applications in ADAS systems, auto-insurance industry. [ABSTRACT FROM AUTHOR]

In recent years, pedestrian and vehicle detection at night has become an important subject of computer vision applications. Because the environment light is weak at night, the common traditional camera-input algorithms often performs poorly. Pedestrians and vehicles in infrared images are usually brighter than the surrounding environment and have salient features. In this paper, a fast saliency map of pedestrian and vehicle targets in infrared images at night is used to achieve rapid acquisition of areas of interest for pedestrians and vehicles. Based on this, we propose a method to refine and separate the target area to obtain accurate pedestrian and vehicle candidate bounding boxes. Specifically, this paper proposed a multi-feature fusion algorithm of pedestrian and vehicle feature extraction combined with support vector machine (SVM) to determine whether the extracted target area really includes pedestrians and vehicles. Our experimental results show that the proposed method can achieve the expected effect of the classification and detection for pedestrians and vehicles at night, and can meet the real-time requirements of actual road scenarios. [ABSTRACT FROM AUTHOR]

This paper solves the problem of designing local adaptive cruise controllers that guarantee collision avoidance of the vehicles in a platoon. Whereas previous studies have solved this problem for specific vehicle models, the present paper shows a general solution for a wider class of linear vehicle models. In particular, the problem of finding a controller that renders a vehicle externally positive to guarantee string stability and collision avoidance is studied in detail. The result is an algorithm that finds a set of closed-loop eigenvalues that correspond to an externally positive feedback loop. The developed methods are applied to an example and the effectiveness is verified in a laboratory experiment with multiple robots driving in a platoon. [ABSTRACT FROM AUTHOR]

In this paper, considering spatiotemporal correlations, we propose a novel short-term traffic flow prediction method that is based on diverse ensemble deep learning. First, a new measurement function of spatial correlation is established, quantifying the spatial correlation of traffic flow parameters. Second, the convolutional neural network model embedded long short-term memory (LSTM-CNN) is used, enhancing the learning ability of the spatiotemporal correlation of traffic flow parameters. Third, according to the network structure, ensemble rules of diverse LSTM-CNNs are constructed, combining a series of different and moderately accurate LSTM-CNN models and improving the robustness of the algorithm. Finally, a dynamic optimization method for the weight parameters of the ensemble elements is proposed to accommodate the changes in the actual road networks. The experiments show that the measuring method of spatial correlations, the ensemble combination rules of diverse LSTM-CNN and the dynamic optimization method of ensemble element weight parameters, which are proposed in this paper, have positive effects on improving the performance of the prediction algorithm. In addition, the short-term traffic flow prediction method proposed in this paper can adapt to changes in the traffic flow. It can obtain a better prediction effect even with a small sample, and the prediction performance is better than that of the other four classical short-term traffic flow prediction methods. [ABSTRACT FROM AUTHOR]

This paper proposes a differential pricing method and a strategy for determining the time node based on the multi-dimensional characteristics of trains by Markov stochastic process, which focus on the study of dynamic differential pricing of the same class of seats with non-parallel trains with different operating characteristics at the same origin-destination. Firstly, an improved machine learning Boosting model Gradient Boosting Decision Tree(GBDT) is proposed to optimize the price discrimination grouping. Secondly, a dynamic simulation mechanism of passengers’ selection under differential pricing is constructed, which breaks through the traditional measurement method to measure the passengers’ sensitivity to price changes. Thirdly, a new method, which uses Bootstrap to get the dynamic feedback for the adaptability of passengers under a differential pricing system, is proposed to determine the iteration nodes of differential pricing. Finally, based on the Markov property of railway passengers’ selection, the paper breaks through the traditional static pricing mechanism and constructs a dynamic differential pricing solution framework. Taking the Beijing-Shanghai high-speed trains from Beijing South to Shanghai Hongqiao as an example, by comparing with the existing dynamic differential pricing system, it’s proved that the dynamic differential pricing system proposed in this paper can balance the occupancy rate between trains, suppress the fluctuation of occupancy rate, determine the updating node of differential pricing, etc., and hence, is more reasonable and effective than the current system. [ABSTRACT FROM AUTHOR]

Urban public transport is a very complex system, and with the development of urbanization, there are many new urban traffic characteristics. Making bus routes and scheduling strategies more efficient, scientific and accurate has a positive impact on the actual operation of public transport. To solve the urban public transport line design problem, this paper describes the implicit law of the characteristics of public transport travel from a deep perspective and analyzes the forms, influencing factors and existing problems of bus dispatching. By establishing a multiobjective public transport dispatching optimization model, starting from bus companies, passengers and government departments, public transportation operating costs comprehensively consider the interests of various parties and finally realize the optimization objective of minimizing fixed costs, fuel costs, carbon emission costs and time window penalty costs. The objective function is set reasonably, and the generation and optimization method of the initial line set in the public transport line design problem is improved; suitable constraint conditions and evaluation indicators are considered. This paper attempts to control the overall length of the bus line on the premise of fully meeting the travel needs of passengers. By solving the multiobjective problem on the same network and comparing different multiobjective optimization algorithms, the effectiveness of the method is evaluated. Additionally, an improved multiobjective adaptive particle swarm optimization (MOAPSO) is proposed, which has the characteristics of faster convergence, higher efficiency and low computational complexity. The simulation experimental results show that the proposed algorithm in this paper can obtain a better Pareto optimal solution set and can effectively solve the multiobjective model. The departure interval conforms to the passenger flow distribution, which can effectively reduce costs and improve the travel service quality of passengers on a large-scale network. [ABSTRACT FROM AUTHOR]

The fifth generation New Radio vehicle-to-everything (5G NR-V2X) has higher requirements for delay and reliability, which brings challenges to the physical layer signal processing. Mobile edge computing (MEC) can provide larger capacity data storage and more efficient computation through localization of on-board unit (OBU) and next generation Node B (gNB) services. This paper combines the channel estimation of New Radio vehicle-to-infrastructure (NR-V2I) communication baseband signal processing with MEC and designs an intelligent channel estimation framework. In the MEC server, this paper proposes a channel estimation algorithm based on deep learning. This algorithm uses a one-dimensional convolutional neural network (1D CNN) to complete frequency-domain interpolation and conditional recurrent unit (CRU) for time-domain state prediction. Additional velocity coding vector and multipath coding vector track changes in the environment, and accurately train channel data in different mobile environments. System simulation and analysis show that the proposed algorithm improves the channel estimation accuracy, reduces the bit error rate, and enhances the robustness compared with the representative channel estimation algorithms for NR-V2I. [ABSTRACT FROM AUTHOR]

In the recent past, platooning evolved into an attractive cooperative driving technology, broadly discussed in research and practice. Vehicles in platoons use cooperative adaptive cruise control to drive at close distances to each other. Platooning (i) increases the capacity of the street by a factor of 2; (ii) reduces the fuel consumption and emissions by up to 20%; and (iii) has social implications as it increases driver comfort and safety. As platooning research progresses, platooning coordination becomes a major research focus. The coordination of platoons, including the assignment of vehicles to platoons, the management of inter- and intra-platoon interactions, and the coordination of interactions with other vehicles is an important step towards an effective usage of platooning in practice. Based on a literature review of 1,600 papers, this survey provides an overview of state of the art in platooning coordination research for both cars and trucks. In this paper, we present a novel taxonomy for platooning coordination and classify existing approaches. We use the results of the literature review to discuss challenges and outline avenues for future work such as multi-objectiveness and individualisation. [ABSTRACT FROM AUTHOR]

Intelligent public transport systems are a key direction for the study of intelligent transportation systems (ITSs), and they perform the following functions: location and tracking, aided navigation, dispatch and command, and dynamic information release; these systems also help travelers determine optimal routes. This paper mainly studies intelligent bus operation optimization by integrating cases and data from business chains, including the optimization of public transport vehicle scheduling according to the characteristics of vehicle scheduling, considers the interests of passengers and public transport companies, and adopts a true-value encoding method with the departure time as the variable goal optimization. In addition, this paper builds a model for the intelligent scheduling problem in public transport based on an enhanced quantum genetic algorithm (EQGA) to find the optimal timetable. This model sets the minimum waiting time cost of passengers and the maximum interests of public transport companies as the goals, restricts the departure interval and two adjacent intervals, and constrains the load factor of passengers. Moreover, based on the analysis of public transport travel characteristics and passenger flow data, this paper evaluates the efficiency of public transport operation, reasonably analyzes the utilization of public transport resources and the travel time of public transport passengers, and thoroughly studies the public transport operation system. This paper selects actual bus line data for empirical analysis, and the empirical results show that the proposed algorithm and model can meet the requirements of many aspects and provides good intelligence, applicability, and optimization. [ABSTRACT FROM AUTHOR]

This paper studies the impact of stochastic load variations on distributed optimal load tracking and allocation (OLTA) problems in cyber-physical DC microgrids (MGs) for transportation electrification. Without load variations, the distributed optimization strategies developed in our earlier work can achieve convergence to global optimal solutions in a multi-objective optimization that balances fair load allocation and power loss reduction. Under persistent stochastic load variations, this paper develops distributed optimal strategies to track time-varying loads under noisy observations and establishes their convergence properties and error bounds. The limiting behavior of the errors characterizes the fundamental impact of the step size on irreducible errors due to conflict between attenuating observation noises and tracking load changes. Optimality conditions and algorithms for selecting the optimal step size are introduced to guide step size selection in practical applications. Simulation studies on real-world systems demonstrate the effectiveness of the proposed algorithms and validate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, a new framework for path tracking is proposed through learning to drive like human beings. Firstly, the imitation algorithm (behavior cloning) is adopted to initialize the deep reinforcement learning (DRL) algorithm through learning the professional drivers’ experience. Secondly, a continuous, deterministic, model free deep reinforcement learning algorithm is adopted to optimize our DRL model on line through trial and error. By combining behavior cloning and deep reinforcement learning algorithms, the DRL model can learn an effective policy quickly for path tracking using some easy-to-measure vehicle state parameters and environment information as inputs. Actor-Critic structure is adopted in the DRL algorithm. In order to speed up the convergence rate of the DRL model and improve the learning effect, we propose a dual actor networks structure for the two different action outputs (steering wheel angle and vehicle speed), and a chief critic network is built to guide the updating process of dual actor networks at the same time. Based on this dual actor networks structure, we can pick out some more important state information as state inputs for different action outputs. Besides, a kind of reward mechanism is also presented for autonomous driving. Finally, simulation training and experiment test are carried out, and the results confirm that the framework proposed in this paper is more than data efficient than the original algorithm, and the trained DRL model can track the reference path with accuracy and has the generalization ability for different roads. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for solving the optimal merging control problem for hybrid electric vehicles (HEVs) under a connected environment. To achieve a reduction in energy consumption and save travel time, this paper focuses on deriving a decentralized feedback control law that provides not only the optimal velocity trajectory for merging but also a torque distribution strategy for the HEV powertrain. For this purpose, a distance domain-based optimal control problem is first proposed to avoid a free end-time cost function formulation that usually arises due to considering the minimization of traveling time. Then, the vehicle dynamics take into account the constraint of the optimization problem to evaluate the energy consumption at the power device level instead of the acceleration, unlike the common method used for reducing energy consumption in previous studies of merging control with linear models. The proposed optimization problem is solved by Pontryagin’s maximum principle, and a traffic-in-the-loop powertrain simulation platform with a real-world emulated traffic scenario and high-fidelity HEV powertrain model is constructed to eliminate the randomly generated merging scenario. Finally, the simulation results obtained on the platform are demonstrated to validate the effectiveness of the proposed decentralized merging control law. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Actual condition-based maintenance for traction power supply equipment (TPSE) is almost based on completely observable equipment state. However, it is unpractical to accurately reveal the equipment state due to the inescapably uncertainty of state assessment. In order to optimize the maintenance of TPSE, a maintenance model based on partially observable Markov decision process is proposed in this paper. Firstly, the degradation process of the TPSE is described by a four-state Markov process, and the state residence time and its transition probability of the equipment are obtained by equaling fault times in the statistical period. Then, the imperfect maintenance is considered in this paper. And the failure risk of the TPSE after maintenance is quantified for optimizing both the economic cost and the reliability of maintenance strategy. Finally, the practical fault record data of 27.5 kV vacuum circuit breakers for a traction power supply system (TPSS) are used to verify the proposed model. The results show that the maintenance model can provide guidance on decision-making for the maintenance under uncertainty, and the determination of maintenance schemes to optimize both TPSE reliability and operational cost. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

The future of autonomous driving is slowly approaching, but there are still many steps to take before it can become a reality. It is crucial to pay attention to road infrastructure, because without it, intelligent vehicles will not be able to operate reliably, and it will never be possible to dispense of driver’s control. This paper presents the work carried out for the detection of road markings damage using computer vision techniques. This is a complex task for which there are currently not many papers and large image sets in the literature. This study uses images from the public Road Damage Detection dataset for the D44 defect and also provides 971 new labelled images for Spanish roads. For this purpose, three detectors based on deep learning architectures (Faster RCNN, SDD and EfficientDet) have been used and single-source and mixed-source models have been studied to find the model that best fits the target images. Finally, F1-score values reaching 0.929 and 0.934 have been obtained for Japanese and Spanish images respectively which improve the state-of-the-art results by 25%. It can be concluded that the results of this study are promising, although the collection of many more images will be necessary for the scientific community to continue advancing in the future in this field of research. [ABSTRACT FROM AUTHOR]

The implementation of autonomous driving is inseparable from developing intelligent driving decision-making models, which are facing high scene complexity, poor decision-making coupling, and the inability to guarantee decision-making safety. This paper starts with the priority and logic of lane change and car-following decision-making, considering driving efficiency, safety, and comfort, then constructs a double-layer decision-making model. This paper uses two deep reinforcement learning algorithms for the upper and lower layers to process large-scale mixed state space and ensure the composite action output of lane-changing decisions and car-following decisions. In the upper layer model, we use the D3QN algorithm to distinguish the potential value of the environment and the value of selecting lane-changing actions when making lane-changing decisions. Different from the traditional mechanisms that only use negative rewards, the lane changing benefit function and dangerous action shielding mechanism are used to eliminate collisions. DDPG algorithm is adopted in the lower layer model to process car-following decisions and output continuous vehicle speed control. Besides, coupled training is taken for the two algorithms to improve the coordination of the double-layer model. This paper selected mixed standard driving cycle conditions to build a highly complex training environment and used NGSIM data to reconstruct scenes to test our model. Simulations in SUMO are presented that the double-layer model can increase the driving speed of the original data by 23.99%, which has higher effectiveness than other models. [ABSTRACT FROM AUTHOR]

A semantic understanding of road traffic can help people understand road traffic flow situations and emergencies more accurately and provide a more accurate basis for anomaly detection and traffic prediction. At present, the overview of computer vision in traffic mainly focuses on the static detection of vehicles and pedestrians. There are few in-depth studies on the semantic understanding of road traffic using visual methods. This paper aims to review recent approaches to the semantic understanding of road traffic using vision sensors to bridge this gap. First, this paper classifies all kinds of traffic monitoring analysis methods from the two perspectives of macro traffic flow and micro road behavior. Next, the techniques for each class of methods are reviewed and discussed in detail. Finally, we analyze the existing traffic monitoring challenges and corresponding solutions. [ABSTRACT FROM AUTHOR]

Accurate estimation of short-term traffic flow, which can help to assist travelers make better route choices, is a significant research field of intelligent transportation system. In order to extract complex spatiotemporal features from a small amount of available traffic data, in this paper we propose a novel Deep Graph Gaussian Processes (DGGPs) for short-term traffic flow prediction. First, in order to accurately describe the relationship between vertices in time series, this paper proposes an attention kernel. Based on this, the Aggregation Gaussian Process uses attention kernel as the covariance function, which overcomes the problem that the existing Gaussian processes and the deep Gaussian processes cannot effectively obtain dynamic spatial features. Second, DGGPs are constructed by the Aggregation Gaussian Process (AGP), the Temporal Convolutional Gaussian Process (TCGP) and the Gaussian process with linear kernel, to solve the existing short-term traffic flow forecasting models cannot obtain complex spatiotemporal features from a small amount of available data. We verify that the attention kernel helps to the proposed model convergence on the three data sets. At the same time, the proposed DGGP can obtain spatiotemporal features from the situation with less available spatial information or temporal information, accurately predict short-term traffic flow, and quantify temporal uncertainty. [ABSTRACT FROM AUTHOR]

The research on loop closure detection has been carried out for the last many years; however, loop closure detection efficiency is still not that good and has been affected by many factors, including illumination conditions, weather conditions, seasons, and viewpoint changes. The research on loop closure detection from a different viewpoint is still an open research problem. The paper proposes an efficient solution to loop closure detection from different viewpoints by using landmarks instead of whole frames and taking the deep and robust features of deep learning instead of handcrafted features. A different kind of training approach is used to train the deep CNN to get highly abstract embeddings of input landmarks. The approach has many advantages over the traditional training approach and can solve many complex problems. This paper has used this approach to force similar or closer embeddings for similar landmarks and is forced to have a large gap in embeddings of different landmarks. The proposed method endeavors viewpoint invariant features, and the astonishing power of deep learning makes the features robust to viewpoint changes, occlusions, and illumination variations. The proposed visual SLAM system is tested on six publicly available datasets, and the results are compared with the most popular Bag of Words methods like DBoW2, DBoW3, and state-of-the-art deep learning methods AlexNet, ResNeXt, FlyNet, AFDPR and Siamese network. The results show that our method is efficient in finding loop closures candidates from different viewpoints. Code is available at https://github.com/IRMVLab/Step-wise-Learning. [ABSTRACT FROM AUTHOR]

Modern complexities associated with an arterial traffic makes existing safety prediction methods insufficient to meet desired standards required by recent developmental needs. This paper proposes an enhanced active safety prediction method based on big-data approach and Stacked AutoEncoder-Gated Recurrent Unit. Firstly, the big-data technology is used to construct a dynamic identification model to recognize real-time operation state and risk state. Secondly, the Stacked AutoEncoder-Gated Recurrent Unit is used to predict a level of safety based on associated recognition results. This paper uses data from working days of Sunset Boulevard, California, from January $1^{\mathrm{st}}$ , 2020, to February $28^{\mathrm{th}}$ , 2020. The results of analysis show that the accuracy of the proposed dynamic recognition model reaches 98.92%, which is better than existing models such as random forest, K-nearest neighbor, and naïve Bayes models. In addition, it is found that the Stacked AutoEncoder-Gated Recurrent Unit can achieve a prediction accuracy of 95.157% and has significant advantages in terms of efficiency. The proposed methods will provide feasible solutions for actively monitoring safety levels. [ABSTRACT FROM AUTHOR]

Bridges play an important role in transportation infrastructure systems. Intelligent and digital management of bridges group is an essential part of the future intelligent transportation infrastructure system. This paper proposes a digital twin system for bridges group in the regional transportation infrastructure network, which is interconnected by measured traffic loads. In physical space, a full-bridge traffic load monitoring system based on information fusion of weigh-in-motion (WIM) and multi-source heterogeneous machine vision is set up on the target bridge to measure traffic loads, also lightweight sensors are employed on the bridges group for structural response information. Furthermore, by establishing mechanical analysis models in the corresponding digital space and using the measured traffic loads as links, the working condition perception and safety warning of all bridges in the regional transportation network is achieved, forming an important support for further intelligent transportation infrastructure system. The proposed digital twin system has been preliminarily implemented in a bridges group around Shanghai, China, demonstrating the feasibility of the technical framework proposed in this paper and the bright prospects. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an efficient way for identifying passengers in a metro system across two heterogeneous but complementary trajectory data sources: AFC data recording two points per trip about when and where a passenger enters or leaves the metro system, and WiFi data recording a few points passed by a passenger in the way of some of his/her trips. The identification result can help us to complete individuals’ mobility, and benefits to lots of services, e.g., individual route choice analysis, epidemic case detecting and so on. The problem is similar to calculate the similarity between two trajectories from two data sources, where a trajectory refers to a sequence of points where a passenger appeared in a metro system on observed days. However, due to the small location space in a metro network, large number of passengers with similar travel pattern, and so on, there are lots of trip overlaps or point co-occurrences between different passengers. That results in a large number of passengers mismatched by existing trajectory similarity measurement. To address the problem, this paper proposes a novel global-local correlation based trajectory similarity measurement GLTC. Specifically, GLTC first extracts all overlapping trip pairs of two trajectories by considering the spatiotemporal inclusions from global level. Then it gets the similarity by aggregating each overlapping trip pair’s local similarity, which is calculated by considering some data-driven insights helpful to uniquely identify a passenger (e.g., uneven passenger flow distribution in different cross-sections of a metro network, the number of trips in same travel pattern of a trajectory, and so on). We evaluate GLTC based on real-world data, and the experimental result shows that GLTC outperforms other baselines. [ABSTRACT FROM AUTHOR]

In recent years, environmental issues have motivated the wide usage of electric vehicles (EVs) due to their zero tailpipe emission. However, this trend can pose severe challenges to power systems, such as decreasing equipment lifetime. Moreover, the CO2 emission of EVs is closer to that of internal combustion engine vehicles in some cases due to the carbon footprint of EV charging. Modeling the uncertain nature of EV users’ behavior is another obstacle due to the complex dynamics of these uncertainties. To overcome these problems, an overarching day-ahead smart charging method is proposed in this paper from the perspective of distribution system operators (DSOs), EV users, and governments simultaneously. The aim of the proposed method is to minimize the operating cost of microgrids, the degradation cost of EV batteries, and emission cost by scheduling the active and reactive power of EV parking lots integrated with photovoltaic (PV) systems as well as finding the optimum network configuration. Previous model-based methods cannot appropriately model uncertainties in EV users’ behavior because of some statistical assumptions. Nevertheless, this paper employs data-driven methods based on generative adversarial networks (GAN) to represent these uncertainties. The performance of the proposed method is evaluated by implementing it on a real reconfigurable microgrid. The results show that using the proposed method, the DSO and emission costs can reduce by 11.96% and 3.37% compared to the uncoordinated charging of EVs, respectively. Furthermore, the share of sustainable energy in EV charging increases by 9% using the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a 3D LiDAR aided global navigation satellite system (GNSS) non-line-of-sight (NLOS) mitigation method due to both static buildings and dynamic objects. A sliding window map describing the environment of the ego-vehicle is first generated, based on real-time 3D point clouds from a 3D LiDAR sensor. Subsequently, the NLOS receptions are detected based on the sliding window map using a proposed quick searching method which eliminates the reliance on the initial guessing of the position of the GNSS receiver. Instead of directly excluding the detected NLOS satellites from further estimating the position, this paper rectifies the pseudo-range measurement model by (1) correcting the pseudo-range measurements if the reflecting point of the NLOS signals is detected within the sliding window map, and (2) remodeling the uncertainty in the NLOS pseudo-range measurement using a novel weighting scheme. The performance of the proposed model was experimentally evaluated in several typical urban canyons in Hong Kong using an automobile-level GNSS receiver. Furthermore, the potential of the proposed NLOS mitigation method in GNSS and the integration of inertial navigation systems were evaluated via factor graph optimization. [ABSTRACT FROM AUTHOR]

The concept of Urban Air Mobility (UAM) proposes to use revolutionary new electrical vertical takeoff and landing (eVTOL) aircraft to provide efficient and on-demand air transportation service between places previously underserved by the current aviation market. A key challenge for the success of UAM is how to manage large-scale autonomous flight operations with safety guarantee in high-density, dynamic and uncertain airspace environments. In this paper, a safety assured decentralized online guidance algorithm with airborne self-separation capability is proposed and analyzed for multi-aircraft autonomous flight operations under uncertainties. The problem is formulated as a multi-agent Markov Decision Process with continuous action space and is solved by a customized decentralized online algorithm based on Monte Carlo Tree Search (MCTS). To guarantee the safety of real-time autonomous flight operations in uncertain environments, the formulation of loss of chance constrained separation is introduced and integrated with the proposed MCTS algorithm. In addition, Gaussian process regression along with Bayesian optimization is employed to discretize the continuous action space, which helps shorten the flight time. A comprehensive numerical study shows that the proposed algorithm can provide safe onboard guidance with guaranteed low near mid-air collision probability in uncertain and high-density airspace environments. [ABSTRACT FROM AUTHOR]

Landing is generally cited as one of the riskiest phases of a flight, as indicated by the much higher accident rate than other flight phases. In this paper, we focus on the hard landing problem (which is defined as the touchdown vertical speed exceeding a predefined threshold), and build a probabilistic predictive model to forecast the aircraft’s vertical speed at touchdown, using DASHlink data. Previous work has treated hard landing as a classification problem, where the vertical speed is represented as a categorical variable based on a predefined threshold. In this paper, we build a machine learning model to numerically predict the touchdown vertical speed during aircraft landing. Probabilistic forecasting is used to quantify the uncertainty in model prediction, which in turn supports risk-informed decision-making. A Bayesian neural network approach is leveraged to construct the predictive model. The overall methodology consists of five steps. First, a clustering method based on the minimum separation between different airports is developed to identify flights in the dataset that landed at the same airport. Secondly, identifying the touchdown point itself is not straightforward; in this paper, it is determined by comparing the vertical speed distributions derived from different candidate touchdown indicators. Thirdly, a forward and backward filtering (filtfilt) approach is used to smooth the data without introducing phase lag. Next, a minimal-redundancy-maximal-relevance (mRMR) analysis is used to reduce the dimensionality of input variables. Finally, a Bayesian recurrent neural network is trained to predict the touchdown vertical speed and quantify the uncertainty in the prediction. The model is validated using several flights in the test dataset, and computational results demonstrate the satisfactory performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper proposes a trajectory optimization approach for high-speed trains to reduce traction energy consumption and increase riding comfort. Besides, the proposed approach can also achieve energy-saving effects by optimizing the operation time between stations. First, an optimization model is developed by defining the objective function as a trade-off function of the traction energy consumption and riding comfort. In addition to constraints in the classic optimal train control model, three new factors–the discrete throttle settings, neutral zones, and sectionalized tunnel resistance–are considered. Then, the model is discretized and turned into a multi-step decision optimization problem. All the nonlinear constraints are approximated using piecewise affine (PWA) functions, and the trajectory optimization problem is turned into a mixed integer linear programming (MILP) problem which can be solved by existing solvers CPLEX and YALMIP. Finally, some case studies with real-world data sets are conducted to present the effectiveness of the proposed approach. The simulation results are compared with the practical running data of trains, which shows that the proposed model and the optimization approach save energy and improve the riding comfort. [ABSTRACT FROM AUTHOR]

In this paper, we present an improved framework for the instance-aware semantic segmentation of road furniture in mobile laser scanning data. In our framework, we first detect road furniture from mobile laser scanning point clouds. Then we decompose the detected pieces of road furniture into poles and their attached components, and extract the instance information of the components with different features. Most importantly, we classify the components into different categories by combining a classifier and a probabilistic graphic model named DenseCRF, which is the major contribution of this paper. For the classification of the components using DenseCRF, the unary potentials and the pairwise potentials are first obtained. The unary potentials are obtained from the classifier which takes the instance information of components as the input. The pairwise potentials are calculated considering contextual relations between components. By utilising DenseCRF, the contextual consistency of components is preserved, and the performance is significantly improved compared to our previous work. We collect three datasets to test our framework, and compare the classification performances of six different classifiers with and without DenseCRF. The combination of random forest with DenseCRF outperforms the other methods and achieves high overall accuracies of 83.7%, 96.4% and 95.3% in these three datasets. Experimental results demonstrate that our framework reliably assigns both semantic information and instance information for mobile laser scanning point clouds of road furniture. [ABSTRACT FROM AUTHOR]

The safety and efficiency of the merging zone is particularly important for traffic networks. Although autonomous vehicle improves the safety and efficiency from vehicle view, traffic controlling in merging zone mostly focus on improving efficiency from roadside view. Lacking of detailed driving recommendation, it ignores the safety of merging zone where commercial vehicle pose a high collision risk in real traffic. This paper proposes a roadside decision-making methodology to simultaneously improve the safety and efficiency of merging zone. We have built two modules, namely assessment and decision-making. Assessment module takes advantage of Bayesian inference to evaluate dynamic collision risk. Decision-making module based on deep reinforcement learning recommends the actions to commercial vehicles by roadside unit. A series of typical simulation tests show that our method increases the TTC of commercial vehicles by an average of 62.7%. In the free flow, the overall travel time of vehicles in the merging zone is reduced by 11.68%. Most notably, when congestion occurred, the average jam length is reduced by 59.68% on the premise of safety. Moreover, the average accuracy of the roadside decision-making method on the evaluation metrics of TTC, travel time, and jam length are 93.73%, 91.65%, and 94.45%, respectively. The experimental results show that the roadside decision-making methodology simultaneously improves safety and efficiency, and it dynamically adapts free and congested traffic flow. [ABSTRACT FROM AUTHOR]

In the Internet of Vehicles (IoV), the classic TCP/IP still plays an important role for data transmission, traffic control and address assignment. However, with increasing requirements on content retrieve efficiency in IoV, the drawbacks of traditional TCP/IP stacks, such as weak scalability in large networks, low efficiency in dense environment and unreliable addressing in high mobility circumstance, have incurred significant performance degradations in vehicular environments. Fortunately, the emerging Named Data Network (NDN) technology provides a good choice to address above issues in vehicular environment by proving content caching capability with introduced content store module, and boosts the research activity of Vehicular Named Data Network (VNDN) in the last few years. In this paper, to improve the service performance, e.g., reducing the delay of data acquisition, a data caching scheme is proposed by taking the spatial-temporal characteristics of data into account. At first, we divided the data in a VNDN into emergency safety message, traffic efficiency message and service message, according to the application requirements. Then, we analyze the spatial-temporal characteristics of these three message categories and design the caching strategy according to these characteristics. Experimental results from NDNSim platform show that our designed scheme has an approximately 50% performance enhancement compared with Leave Copy Everywhere (LCE), Pro(0.7), and Pro(0.2) data caching protocols in terms of average hit rate, average hop count and average cache replacement times, which verifies the reliability and effectiveness of our proposed data caching scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a human-robot interaction (HRI) framework to reduce the vulnerability of an unmanned ground vehicle (UGV) platoon under cyber attacks. An observer-based autonomous resilient control strategy is first designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. Rigorous proof of the safety conditions of the platoon under V2V cyber attacks is derived. Next, to facilitate human supervision in emergencies and uncertainties, a decision-making aid system is developed, which consists of an anomaly reporting system (ARS), a trust-based information management system (TIMS), and a graphical user interface (GUI). ARS is designed to detect the abnormality of the neighbor UGVs based on the model-based residual generation and analysis. TIMS is developed to rule out information with low trustworthiness, which is fulfilled by a Bayesian-based information pre-processor followed by an enhanced median filter with trustworthiness regulation. It is rigorously proved that TIMS can recover the tempered information under the mild cyber attack scenario autonomously. To deal with unexpected severe vehicle-to-cloud (V2C) cyber attacks, human observations can also be leveraged to bolster the resilience of the TIMS. Representative simulation and humans-in-the-loop experiment (fulfilled by the designed GUI) demonstrate that the proposed framework can effectively guide human operators when working with UGV platoons under cyber attacks. On average, compared to a multi-screen surveillance benchmark approach, the proposed framework can achieve 78.4% decrements of platoon vulnerability and 39.0% decrements in human workload. [ABSTRACT FROM AUTHOR]

Usually, the detection process of traffic objects, such as vehicles, finds the visual input lacks the necessary depth information, so it is difficult to directly and quickly obtain the results. Typically, to separate the objects from the complex background, it is necessary to utilize a complex model or prior knowledge, which can be computationally expensive or simply infeasible. To battle this issue, depth visual information is used in this paper to accurately segment roads and vehicles, so it doesn’t need to use complex models to detect objects in the visual input. First, an unsupervised deep learning-based monocular depth estimation method is used to obtain the stereo disparity map. Then a non-parametric, refined U-V disparity mapping method is used to obtain the road region of interest. Next, this paper uses the road parallel scanning to determine the source and vanishing points and uses the adjacent disparity similarity algorithm to complement and extract the target region to detect roads and vehicles. This algorithm uses multi-feature fusion such as height-width ratio, perspective ratio, and area ratio to accurately segment the target region, and the effectiveness of the proposed method is tested on a public dataset. The experimental results show that the proposed model can accurately and efficiently detect roads and vehicles in a variety of scenarios. [ABSTRACT FROM AUTHOR]

Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving/driver assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price. The latest progress in this field has called for a review to sum up the conventional knowledge of ADAS, the state-of-the-art researches, novel applications and standards in real world. With the help of this kind of review, newcomers in this field can get basic knowledge easier and other researchers may be inspired with potential future development possibility. This paper makes a general introduction about ADAS by analyzing its hardware support, computation algorithms and current development state. Different types of perception sensors are introduced from their interior feature classifications, installation positions, supporting ADAS functions, and pros and cons. The comparisons between different sensors are concluded and illustrated from their inherent characters and specific usages serving for each ADAS function. The current algorithms for ADAS functions are also collected and briefly presented in this paper from both traditional methods and novel ideas. Additionally, discussions about current regulations and market state of ADAS in China are reviewed in this paper, and other open issues related to ADAS are also introduced in particular. [ABSTRACT FROM AUTHOR]

End-to-end steering estimation is one of the important deep regression tasks. However, driving datasets are always imbalanced on the distribution of the steering value, which makes end-to-end learning models perform bad steering estimation accuracy, especially for the sharp steering value estimation, which finally, leads to an imbalanced training problem. In this paper, the essential reason for the imbalanced training problem for the deep regression task is first revealed as the gradient disharmony. To hedge the gradient disharmony, a general gradient balanced mechanism for the deep regression task is proposed in this paper. Meanwhile, the general gradient balanced mechanism is applied for the steering estimation task. According to the steering distribution, a novel Adaptive Gradient Balanced Mechanism (AGBM) is proposed to hedge the gradient disharmony based on the Laplace distribution with the general gradient balanced mechanism theory. Further, a new loss function embedding with AGBM is proposed to balance the gradient disharmony for the training process. AGBM makes the process of gradient hedging with adaptive style without parameters finetuning. Based on five driving datasets, four experiments are conducted for demonstrations. The experiment results show the AGBM enables the end-to-end models to perform the best accuracy of the steering estimation, including the sharp steering samples. Meanwhile, AGBM is generalized with different end-to-end learning models. [ABSTRACT FROM AUTHOR]

Random traffic flow load (TFL) simulation is an important analysis method for bridge design and safety assessment, and accurate TFL modelling is a prerequisite for high-quality simulation. The existing TFL modelling methods almost all rely on the load data monitored by the weigh-in-motion system (WIM system). However, the WIM system has natural defects such as unsatisfactory measurement accuracy at low speed and the inability to measure vehicle lengths and transverse positions in the lane, limiting the improvement of TFL simulation accuracy. Regarding this, a TFL monitoring system that integrates the functions of machine vision and WIM system is developed in this paper. In this system, a deep learning method is applied, for the accurate detection of vehicles and wheels in the video, and the extraction of key parameters for TFL modelling based on detection results. According to the long-term monitoring value, statistical distributions of key parameters are determined, and then an intelligent TFL model is derived from the Intelligent Driver Model (IDM), considering the car-following behavior of vehicles. Correspondingly, this paper further suggests a TFL simulation method and achieves an accurate TFL simulation. A cable-stayed bridge is taken as an example to verify the feasibility of the method. The results show that, compared to the modelling and simulation methods that only rely on the WIM system, the proposed method not only reduces the measurement error of vehicle dimensions by nearly 4 times, but also performs higher resolution in time measurement. The proposed method effectively overcomes the shortcomings of existing schemes and has good application potential in engineering. [ABSTRACT FROM AUTHOR]

Tracking control is one of the important working conditions of unmanned driving and can help vehicles keep distances and run in an orderly manner to improve traffic utilization, ensure clear roads and avoid rear-end collisions. This paper constructs the motion trend of obstacles within the predictive step length of model predictive control (MPC), designs the danger level indicator between vehicles and obstacles, and formulates dynamic planning for the original reference path based on MPC. This paper proposes an integrated collision avoidance control strategy based on dynamic nonlinear MPC (DNMPC), and predicts the locations of moving obstacles within the predictive step length of DNMPC. The proposed method can perform initial planning for the collision-avoidance path according to the road environment information and based on the activation function. It builds the function for the tendency of obstacles within the predictive step length of MPC, introduces it into the objective function for optimization, designs dynamic path planning control based on the theories of MPC, and performs local optimization to the initial reference path under the obstacles in motion with a mass model. In addition, it defines varying discrete step lengths within the predictive step length and achieves the long-distance prediction and high-precision control of collision avoidance controllers. The experimental results show that the dynamic, nonlinear, and integrated collision avoidance control proposed in this paper can ensure excellent collision avoidance and steady vehicle driving and has very good practical value. [ABSTRACT FROM AUTHOR]