Intelligent Perception Control System of Railway Level Crossing Gate Based on TRIZ Theory

TRIZ theory is an innovative method to analyse problems and solve them, which is widely used in many fields. In this paper, TRIZ theory is used to improve the design of railway crossing guardrail system. The use of nine-screen analysis, functional analysis, cause-effect chain analysis and other tools to analyse the problem of poor manual control effect in the railway crossing guardrail system, the use of technical contradictions, physical contradictions and other tools to improve the system design, effectively reduce the possibility of danger when cars and pedestrians cross railway crossings, improve the traffic safety and traffic order of the railway level crossing, and reduce the work burden of railway crossing caretakers

A review of the technological developments for interlocking at level crossing

A Level Crossing remains as one of the highest risk assets within the railway system often depending on the unpredictable behaviour of road and footpath users. For this purpose, interlocking through automated safety systems remains a key area for investigation. Within Europe, 2015–2016, 469 accidents at crossings were recorded of which 288 lead to fatalities and 264 lead to injuries. The European Union’s Agency for Railways has reported that Level Crossing fatalities account for just under 28% of all railway fatalities. This paper identifies suitable obstacle detection technologies and their associated algorithms that can be used to support risk reduction and management of Level Crossings. Furthermore, assessment and decision methods are presented to support their application. Finally state of the art and synergistic opportunity of which a combination of obstacle detection sensors with intelligent decisions layers such as Deep Learning are discussed which can provide robust interlocking decisions for rail applications. The sensor fusion of video camera and RADAR is a promising solution for Level Crossings. By applying additional sensing techniques such as RADAR imaging, further capabilities are added to the system, which can lead to a more robust approach

Application of Advanced Analytic and Risk Techniques to Railroad Operations Safety and Management

69A3551847102Railroads generate large amounts of data. The data collected by railroads are in several different forms including both numeric and textual data. Further, there are numerous external databases that contain information and data relevant to railroad maintenance, operations, and capital investments. The fundamental problem with the amounts of data and varied data sources is that railroads have generally lacked tools and the capability to analyze these data to develop predictive models to improve decisions regarding maintenance, operations, and capital investments that improve safety, service and, ultimately, overall profitability. This is particularly a problem for Class II and III short line railroads that lack significant staff and resources to undertake these analyses. This project specifically addresses these problems with two Class II railroads in terms of grade crossing and trespassing incidents and identifying potential transload customers for specific commodities. For one railroad partner, high-risk grade crossing and trespassing situations are identified, and potential risk reduction measures are recommended. For the other railroad partner, potential customers for two possible transload commodities are identified and data sources provided for further analyses. A prototype decision support system (DSS) is proposed, and advanced data visualization tools are demonstrated and applied for both railroads. Recommendations for further research and development are made specifically for grade crossing and trespassing risk metrics and profiles

Implementation Report of the USDOT Grade Crossing Safety Task Force

On March 1, 1996, the U.S. Department of Transportation (US DOT) Grade Crossing Safety Task Force delivered a report entitled Accidents That Shouldn't Happen to Transportation Secretary Federico Pena. Secretary Pena had directed that the Task Force be convened to address factors that might have contributed to a fatal collision involving a commuter train and a school bus in Fox River Grove, Illinois, in October 1995. In its report, the Task Force addressed safety problems that were not specifically covered in the Department's 1994 Rail-Highway Crossing Safety Action Plan: Interconnected Signals; Vehicle Storage Space; High-Profile Crossings; Light-Rail Transit Crossings; and Special Vehicle Operations. The report made 24 recommendations to remedy physical and procedural deficiencies in grade crossing construction, operation, maintenance, funding, enforcement, coordination, information, standards, and education. The principal finding of the Task Force report was that "improved highway-rail grade crossing safety depends upon better cooperation, communication, and education among responsible parties if accidents and fatalities are to be reduced significantly." With this in mind, the report proposed a status update: "The Task Force will reconvene one year after issuance of this report to evaluate progress in implementation of its recommendations." The Task Force fulfilled this recommendation on March 1, 1997, by delivering an interim report on the Department's progress to the Associate Deputy Secretary and Director of the Office of Intermodalism, Michael P. Huerta. The contents of this interim report have been incorporated as the first chapter of this document to give the reader a comprehensive overview of Departmental actions in implementing Task Force recommendations. The Task Force report proposed that "The FHWA will meet with the FRA to develop the process for implementing the FHWA long-term recommendation to convene a technical working group to evaluate current standards and guidelines for a variety of grade crossing technical issues. Selection of working group members and development of an implementation schedule should be accomplished by June 1, 1996, with the group's product targeted for completion by June 1, 1997 ." Among the noteworthy accomplishments of the USDOT Task Force are the convening of a Technical Working Group (TWG) that has made 35 recommendations for standards, guidelines and other grade crossing safety issues; the identification of focal points to coordinate railroad safety issues in each State; the initiation of regional State/railroad conferences; and the creation of an advance warning sign for motorists approaching high-profile crossings. All of the Task Force activities and accomplishments including the above are detailed in Chapter 1. Chapter 2 focuses on the accomplishments of the TWG. Among the noteworthy accomplishments of the TWG are development of uniform terms for railroad and traffic engineers; development of an interconnected warning placard for controller cabinets; and recommendations in the areas of interconnected signals, vehicle storage, joint inspections, and high-profile crossings. This report to Transportation Secretary Rodney E. Slater summarizes the technical working group's findings on improved standards and guidelines for railroad-highway grade crossing safety. In making this report, the Task Force reaffirms the Secretary's commitment to make transportation safety the Department's highest priority. The Department intends to distribute this report to all who participated in the TWG. By distributing this report, the Department urges those agencies, organizations, and other professional societies that participated in its compilation to take steps to formally endorse this report and implement its recommendations. The Department further recommends that the report's terminology for railroad-highway grade crossings be adopted and used as soon as possible in correspondence, training initiatives, and in new or revised railroad-highway grade crossing publications

Object detection at level crossing using deep learning

Multiple projects within the rail industry across different regions have been initiated to address the issue of over-population. These expansion plans and upgrade of technologies increases the number of intersections, junctions, and level crossings. A level crossing is where a railway line is crossed by a road or right of way on the level without the use of a tunnel or bridge. Level crossings still pose a significant risk to the public, which often leads to serious accidents between rail, road, and footpath users and the risk is dependent on their unpredictable behavior. For Great Britain, there were three fatalities and 385 near misses at level crossings in 2015–2016. Furthermore, in its annual safety report, the Rail Safety and Standards Board (RSSB) highlighted the risk of incidents at level crossings during 2016/17 with a further six fatalities at level crossings including four pedestrians and two road vehicles. The relevant authorities have suggested an upgrade of the existing sensing system and the integration of new novel technology at level crossings. The present work addresses this key issue and discusses the current sensing systems along with the relevant algorithms used for post-processing the information. The given information is adequate for a manual operator to make a decision or start an automated operational cycle. Traditional sensors have certain limitations and are often installed as a “single sensor”. The single sensor does not provide sufficient information; hence another sensor is required. The algorithms integrated with these sensing systems rely on the traditional approach, where background pixels are compared with new pixels. Such an approach is not effective in a dynamic and complex environment. The proposed model integrates deep learning technology with the current Vision system (e.g., CCTV to detect and localize an object at a level crossing). The proposed sensing system should be able to detect and localize particular objects (e.g., pedestrians, bicycles, and vehicles at level crossing areas.) The radar system is also discussed for a “two out of two” logic interlocking system in case of fail-mechanism. Different techniques to train a deep learning model are discussed along with their respective results. The model achieved an accuracy of about 88% from the MobileNet model for classification and a loss metric of 0.092 for object detection. Some related future work is also discussed

Safety Enhancements at Short-Storage-Space Railroad Crossings

2019-1033A short storage space railroad crossing has insufficient distance between the crossing and the highway intersection stop line to safely store a design vehicle. The current Michigan Department of Transportation (MDOT) project (OR 19-032), led by Texas A&M Transportation Institute (TTI), aimed to provide guidance on these crossing types. This study examined the crash data, surroundings of the crossing, and driver behavioral patterns using naturalistic driving and simulation data to identify suitable countermeasures and build communication between MDOT Traffic & Safety and Office of Rail to refine the guidelines. This project conducted four major tasks: 1) a comprehensive review on short-storage crossing related studies, 2) data preparation and development of safety indices for short-storage crossing locations, 3) development of driver behavior related safety scoring for different passive treatments using data from SHRP-2 naturalistic driving study (NDS) and simulation, and 4) development of a guidance document and final report on short-storage crossings in Michigan. The findings of literature review show that little research has been conducted to empirically evaluate the effectiveness of different treatments for grade crossings with short storage, outside of traffic signal preemption strategies. The results also revealed that the highest number of crashes occurred in southeast Michigan (Detroit area), and the lowest number occurred in the sparsely populated northern part of the state. The findings also show that short storage locations are mostly on local undivided roadways. Both NDS and simulation study results indicate that drivers face difficulties in following signs and markings near the short storage locations. This study can help authorities in identifying the issues of driver-related distraction and sign following patterns and can aid in improving short storage crossing safety. In addition, this project provided recommendations for modifications to the language in six relevant MDOT publications

A New Form of Interlocking Developing Technology for Level Crossings and Depots with International Applications

There are multiple large rail infrastructure projects planned or currently being undertaken within the United Kingdom. Many of these projects aim to reduce the continual issue of limited or overcapacity service. These projects involve an expansion of Rail lines, introducing faster lines, improved stations in towns and cities and better communication networks. Some major projects like Control Period 6 (CP6) are being managed by Network Rail; where projects are initiated throughout Great Britain. Many projects are managed outside Great Britain e.g., Trans-European Transport Network Program, which is planning for expansion of Rail lines (almost double) for High-Speed Rails (category I and II). These projects will increase the number of junctions and Level Crossings. A Level Crossing is where a Rail Line is crossed by a road or a walkway without the use of a tunnel or bridge. The misuse from the road users account for nearly 90% of the fatalities and near misses at Level Crossings. During 2016/2017, the Rail Network recorded 6 fatalities, about 400 near-misses and more than 77 incidents of shock and trauma. Accidents at Level Crossings represent 8% of the total accidents from the whole Rail Network. Office of Rail and Road (ORR) suggested that among these accidents at Level Crossings 90% of them are pedestrians. Such high numbers of accidents, fatalities and high risk have alarmed authorities. These authorities found it necessary to invest time and utilise given resources to improve the safety system at a Level Crossing using the safer and reliable interlocking system. The interlocking system is a feature of a control system that makes the state of two functions mutually independent. The primary function of Interlocking is to ensure that trains are safe from collision and derailment. Considering the risk associated with the Level Crossing system, the new proposed interlocking system should utilise the sensing system available at a Level Crossing to significantly reduce implementation cost and comply with the given standards and Risk Assessments. The new proposed interlocking system is designed to meet the “Safety Integrity Level- SIL” and possibly use the “2oo2” approach for its application at a Level Crossing, where the operational cycle is automated or train driver is alarmed for risk situations. Importantly, the new proposed system should detect and classify small objects and provide a reasonable solution to the current risk associated with Level Crossing, which was impossible with the traditional sensing systems. The present work discusses the sensors and algorithms used and has the potential to detect and classify objects within a Level Crossing area. The review of existing solutions e.g Inductive Loops and other major sensors allows the reader to understand why RADAR and Video Cameras are preferable choices of a sensing system for a Level Crossing. Video data provides sufficient information for the proposed algorithm to detect and classify objects at Level Crossings without the need of a manual “operator”. The RADAR sensing system can provide information using micro-Doppler signatures, which are generated from small regular movements of an obstacle. The two sensors will make the system a two-layer resilient system. The processed information from these two sensing systems is used as the “2oo2” logic system for Interlocking for automating the operational cycle or alarm the train drive using effective communication e.g., GSM-R. These two sensors provide sufficient information for the proposed algorithm, which will allow the system to automatically make an “intelligent decision” and proceed with a safe Level Crossing operational cycle. Many existing traditional algorithms depend on pixels values, which are compared with background pixels. This approach cannot detect complex textures, adapt to a dynamic background or avoid detection of unnecessary harmless objects. To avoid these problems, the proposed work utilises “Deep Learning” technology integrated with the proposed Vision and RADAR system. The Deep Learning technology can learn representations from labelled pixels; hence it does not depend on background pixels. The Deep 3 | P a g e Learning technology can classify, detect and localise objects at a Level Crossing area. It can classify and differentiate between a child and a small inanimate object, which was impossible with traditional algorithms. The system can detect an object regardless of its position, orientation and scale without any additional training because it learns representation from the data and does not rely on background pixels. The proposed system e.g., Deep Learning technology is integrated with the existing Vision System and RADAR installed at a Level Crossing, hence implementation cost is significantly reduced as well. The proposed work address two main aspects of training a model using Deep Learning technology; training from scratch and training using Transfer Learning techniques. Results are demonstrated for Image Classification, Object Detection and micro-Doppler signals from RADAR. An architecture of Convolutional Neural Network from scratch is trained consisting of Input Layer, Convolution, Pooling and Dropout Layer. The model achieves an accuracy of about 66.78%. Different notable models are trained using Transfer Learning techniques and their results are mentioned along with the MobileNet model, which achieves the highest accuracy of 91.9%. The difference between Image Classification and Object Detection is discussed and results for Object Detection are mentioned as well, where the Loss metrics are used to evaluate the performance of the Object Detector. MobileNet achieves the smallest loss metric of about 0.092. These results clearly show the effectiveness and preferability of these models for their applicability at Level Crossings. Another Convolutional Neural Network is trained using micro-Doppler signatures from the Radar system. The model trained using the micro-Doppler signature achieved an accuracy of 92%. The present work also addresses the Risk Assessment associated with the installation and maintenance of the system using Deep Learning technology. RAMS (Reliability, Availability, Maintainability and Safety) management system is used to address the General and Specific Risks associated with the sensing system integrated with the Deep Learning technology. Finally, the work is concluded with the preferred choice, its application, results and associated Risk Assessment. Deep Learning is an evolving field with new improvements being introduced constantly. Any new challenges and problems should be monitored regularly. Some future work is discussed as well. To further improve the model's accuracy, the dataset from the same distribution should be gathered with the cooperation of relevant Railway authorities. Also, the RADAR dataset could be generated rather than simulated to further include diversity and avoid any biases in the dataset during the training process. Also, the proposed system can be implemented and used in different applications within the Rail Industry e.g., passenger census and classification of passengers at the platform as discussed in the work

Field Evaluation of Smart Sensor Vehicle Detectors at Railroad Grade Crossings���Volume 4: Performance in Adverse Weather Conditions

The performance of a microwave radar system for vehicle detection at a railroad grade crossing with quadrant gates was evaluated in adverse weather conditions: rain (light and torrential), snow (light and heavy), dense fog, and wind. The first part of this report compares the results of the modified system setup in adverse weather conditions with those from good weather conditions (as presented in Volume 3 of this study). Then, the results of a re-modified system setup were compared to the results for the modified system setup in good and adverse weather conditions. The re-modification was in response to increased detection errors in adverse weather conditions. With the modified setup, system performance was sensitive to the adverse weather conditions. In torrential rain, false calls increased to 24.82%–27.08% (e.g., May 28 and June 1) when there was some traffic on the crossing. However, when there was torrential rain but only one vehicle (e.g., May 31) or no traffic flow (e.g., June 10), the radar units generated 15 false calls on each of those 2 days. For all heavy snow datasets combined, missed calls by a single radar unit and by the two radar units working as a combined unit (i.e., systemwide) represented 13.51% and 11.66% of the loop calls, respectively. The most severe snow effects were found during freezing rain/ice. In dense fog, false calls increased to 11.58%, and all false calls were generated when the gates were moving or in the down position. Wind did not affect system performance, and the errors were similar to those in good weather conditions. With the re-modified setup, the frequency of errors in heavy rain and heavy snow conditions was reduced and system performance was similar to the good weather, light rain, and light snow conditions. In heavy rain, false calls in the re-modified setup were reduced to 2.6% compared with 30.5% in the modified setup. This reduction was the result of a significant decrease in the false calls generated without objects in the crossing. The re-modified setup eliminated the systemwide missed calls in heavy snow. The re-modified setup also reduced the false calls to less than 1% in good weather, light rain, and light snow conditions and practically had no missed, stuck-on, or dropped calls. Results indicate that re-modifications improved the performance of detection system.Illinois Department of Transportation, R27-095Ope

Real-time Road Obstacle Detection Using Association and Symmetry Recognition

This paper presents a fast road obstacle detection system based on association and symmetry. This approach consists to exploit the edges extracted from consecutive images acquired by a stereo sensor embedded in a moving vehicle. The algorithm contains three main components: edges detection, association detection and symmetry calculation. The edges detection is achieved by using the canny operator and point corner to extract all possible edges of different objects at the image. The association technique is used to exploit relationship between the edges of two consecutives images by combining it with the moment operator. The symmetry is used as road obstacle validation; the road obstacles like vehicle and pedestrian have a vertical symmetry. The proposed approach has been tested on different images. The provided results demonstrate the effectiveness of the proposed method