The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.

Data-Driven Intelligent Transportation Systems: A Survey

Map-matching algorithms integrate positioning data with spatial road network data (roadway centrelines) to identify the correct link on which a vehicle is travelling and to determine the location of a vehicle on a link. A map-matching algorithm could be used as a key component to improve the performance of systems that support the navigation function of intelligent transport systems (ITS). The required horizontal positioning accuracy of such ITS applications is in the range of 1 m to 40 m (95%) with relatively stringent requirements placed on integrity (quality), continuity and system availability. A number of map-matching algorithms have been developed by researchers around the world using different techniques such as topological analysis of spatial road network data, probabilistic theory, Kalman filter, fuzzy logic, and belief theory. The performances of these algorithms have improved over the years due to the application of advanced techniques in the map matching processes and improvements in the quality of both positioning and spatial road network data. However, these algorithms are not always capable of supporting ITS applications with high required navigation performance, especially in difficult and complex environments such as dense urban areas. This suggests that research should be directed at identifying any constraints and limitations of existing map matching algorithms as a prerequisite for the formulation of algorithm improvements. The objectives of this paper are thus to uncover the constraints and limitations by an in-depth literature review and to recommend ideas to address them. This paper also highlights the potential impacts of the forthcoming European Galileo system and the European Geostationary Overlay Service (EGNOS) on the performance of map matching algorithms. Although not addressed in detail, the paper also presents some ideas for monitoring the integrity of map-matching algorithms. The map-matching algorithms considered in this paper are generic and do not assume knowledge of ‘future’ information (i.e. based on either cost or time). Clearly, such data would result in relatively simple map-matching algorithms.

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Current map-matching algorithms for transport applications: State-of-the art and future research directions

High-speed railway (HSR) brings convenience to peoples' lives and is generally considered as one of the most sustainable developments for ground transportation. One of the important parts of HSR construction is the signaling system, which is also called the “operation control system,” where wireless communications play a key role in the transmission of train control data. We discuss in detail the main differences in scientific research for wireless communications between the HSR operation scenarios and the conventional public land mobile scenarios. The latest research progress in wireless channel modeling in viaducts, cuttings, and tunnels scenarios are discussed. The characteristics of nonstationary channel and the line-of-sight (LOS) sparse and LOS multiple-input-multiple-output channels, which are the typical channels in HSR scenarios, are analyzed. Some novel concepts such as composite transportation and key challenging techniques such as train-to-train communication, vacuum maglev train techniques, the security for HSR, and the fifth-generation wireless communications related techniques for future HSR development for safer, more comfortable, and more secure HSR operation are also discussed.

Challenges Toward Wireless Communications for High-Speed Railway

The development of light detection and ranging, Radar, camera, and other advanced sensor technologies inaugurated a new era in autonomous driving. However, due to the intrinsic limitations of these sensors, autonomous vehicles are prone to making erroneous decisions and causing serious disasters. At this point, networking and communication technologies can greatly make up for sensor deficiencies, and are more reliable, feasible and efficient to promote the information interaction, thereby improving autonomous vehicle’s perception and planning capabilities as well as realizing better vehicle control. This paper surveys the networking and communication technologies in autonomous driving from two aspects: intra- and inter-vehicle. The intra-vehicle network as the basis of realizing autonomous driving connects the on-board electronic parts. The inter-vehicle network is the medium for interaction between vehicles and outside information. In addition, we present the new trends of communication technologies in autonomous driving, as well as investigate the current mainstream verification methods and emphasize the challenges and open issues of networking and communications in autonomous driving.

Networking and Communications in Autonomous Driving: A Survey

A speed trajectory profile indicating the authorized train speed at each position can be used to guide the driver or the automatic train operation (ATO) system to operate the train more efficiently, which is the most important part of the Chinese Train Control System (CTCS) and will decide the safety and efficiency of train operation. The efforts produced by the train to follow the speed trajectory will directly affect the evaluation of train operation. This paper studies the optimization approach for the speed trajectory of high-speed train in a single section. First, we take the energy consumption as the measure of satisfaction of the railway company, and the trip time is being regarded as the passenger satisfaction criterion; then, we present optimal speed trajectory searching strategies under different track characteristics by dividing the section into some subsections according to different speed limitations. After that, we develop a multiobjective optimization model for the speed trajectory, which is subject to the constraints such as safety requirement, track profiles, passenger comfort, and the dynamic performance. For obtaining the Pareto frontier of train speed trajectory, which has equal satisfaction degree on all the objects, a hybrid evolutionary algorithm is designed and applied to solve the model based on the differential evolution and simulating annealing algorithms. By showing some numerical results of simulations, the efficiency of the proposed model and solution methodology is illustrated.

Multiobjective Optimization for Train Speed Trajectory in CTCS High-Speed Railway With Hybrid Evolutionary Algorithm

Vehicle navigation system estimates vehicle location from Global Positioning System (GPS) or dead-reckoning (DR) system However, because of unknown GPS noise, the estimated location has undesirable errors The purpose of map matching (MM) algorithm is to locate the inaccuracy position of the vehicle to the map data that is referenced by the system In this paper, a simple map-matching algorithm is discussed first Then Kalman filtering and Dempster-Shafer (D-S) theory of evidence are introduced into the improved map-matching algorithm The real road experiments demonstrate the effectiveness and applicability of the improved algorithm, and it is found to produce better results

An improved map-matching algorithm used in vehicle navigation system

Cooperative localization of the connected vehicles is significant for many advanced intelligent transportation system (ITS) applications. Vehicle-to-vehicle communication using dedicated short-range communication (DSRC) has great potential to enhance global navigation satellite systems (GNSSs) for the capability of cooperative localization. In the integration of DSRC and GNSS, the tolerance against the unknown and time-varying observation conditions is a key factor to fulfill the requirements of several specific ITS applications. Under a GNSS/DSRC integrated architecture for cooperative localization, a novel robust cubature Kalman filter (CKF) is proposed in this paper to improve the performance of the data fusion under uncertain sensor observation environments. In the proposed solution, the structure of the standard CKF is enhanced using the Huber M-estimation technique, in which the original measurement update in the CKF is modified considering the probable anomalies in state estimation. Furthermore, based on the investigation of the adjustment effect from the constraint factor, an adaptive strategy for this parameter is introduced to optimize the performance comprehensively. The proposed method is validated using a specific simulation system. Results of experiment and simulations demonstrate the capability of improving the robustness and adaptive performance over the original filters under the unknown operation conditions.

Cooperative Localization of Connected Vehicles: Integrating GNSS With DSRC Using a Robust Cubature Kalman Filter

The cooperative energy-efficient trajectory planning for multiple high-speed train movements is considered in this paper. We model all the high-speed trains as the agents that can communicate with others and propose a local trajectory planning control model using the Model Predictive Control (MPC) theory. After that we design an online distributed cooperative optimization algorithm for multiple train trajectories planning, under which each train agent can regulate the trajectory planning procedure to save energy using redundancy trip time through tuning ACO’s heuristic information parameter. Compared to the existing literature, the vital distinctions of our work lies not only on the online cooperative trajectory planning but also on the distributed mechanism for multiple high-speed trains. Experimental studies are given to illustrate the effectiveness of the proposed methods with the practical operational data of Wuhan-Guangzhou High-speed Railway in China.

Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements

Trajectory planning plays a crucial role in train operation by providing with the authorized speed at each position. The traditional static train trajectory planning methods are always designed offline according to a preplanned timetable, and they ignored the uncertainties of parameters, resulted by line condition, resistance coefficient, and delay. These uncertain disturbances have not been considered adequately in previous studies. This paper deals with the dynamic optimal train trajectory planning problem with uncertainties. First, in order to identify uncertain resistance coefficients and calculate the dynamic limited speed, we present the optimization framework using onboard equipment such as a global navigation satellite system (GNSS) terminal, a power supply system, and a communication device to sample the real-time traffic information. Then, by taking the energy consumption and punctuality as objectives, we propose a moving horizon train trajectory planning optimization model with an adaptive weight allocation mechanism based on trip time error. The innovation of this paper lies not only in the establishment of a novel dynamic optimization model for train trajectory planning but also the strategy that combines real-time traffic information with the trajectory planning procedure. By contrast with most existing solutions, the proposed approach fully takes advantage of the real-time information and thus avoids the difficulties for modeling the uncertain coefficients for train trajectory planning. The efficiency of the proposed approach is illustrated by showing some numerical results of simulations with the infrastructure data from Beijing-Shanghai High-speed Railway of China.

Baigen Cai

Papers

Multiobjective Optimization for Train Speed Trajectory in CTCS High-Speed Railway With Hybrid Evolutionary Algorithm

An improved map-matching algorithm used in vehicle navigation system

Cooperative Localization of Connected Vehicles: Integrating GNSS With DSRC Using a Robust Cubature Kalman Filter

Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements

Moving Horizon Optimization of Dynamic Trajectory Planning for High-Speed Train Operation