Christian Wullems

Towards context-aware security: an authorization architecture for intranet environments

We introduce a context-aware authorization architecture that is designed to augment existing network security protocols in an intranet environment. It describes the architecture components, the proposed extensions to RBAC that facilitate context-aware access control policy, details of the prototyped implementation, and a number of performance results.

Improving level crossing safety through enhanced data recording and reporting: the CRC for rail innovation’s baseline rail level crossing video project

This paper describes the work being conducted in the Baseline Rail Level Crossing Video project, supported by the Australian rail industry and the Cooperative Research Centre for Rail Innovation. The paper discusses the limitations of near-miss data for analysis obtained using current level crossing occurrence reporting practices. The project is addressing these limitations through the development of a data collection and analysis system with an underlying level crossing accident causation model. An overview of the methodology and improved data recording process are described. The paper concludes with a brief discussion of benefits this project is expected to provide the Australian rail industry.

Near-Miss Event Detection at Railway Level Crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU

Video Analytics for the Detection of Near-Miss Incidents on Approach to Railway Level Crossings

116 million annually. To better understand the causal factors of these accidents, a video analytics application is being developed to automatically detect near- miss incidents using forward facing videos from trains. As near-miss events occur more frequently than collisions, by detecting these occurrences there will be more safety data available for analysis. The application that is being developed will improve the objectivity of near- miss reporting by providing quantitative data about the position of vehicles at level crossings through the automatic analysis of video footage. In this paper we present a novel method for detecting near-miss occurrences at railway level crossings from video data of trains. Our system detects and localizes vehicles at railway level crossings. It also detects the position of railways to calculate the distance of the detected vehicles to the railway centerline. The system logs the information about the position of the vehicles and railway centerline into a database for further analysis by the safety data recording and analysis system, to determine whether or not the event is a near-miss. We present preliminary results of our system on a dataset of videos taken from a train that passed through 14 railway level crossings. We demonstrate the robustness of our system by showing the results of our system on day and night videos.

Improving Near-Miss Event Detection Rate at Railway Level Crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU

A Human Factors Investigation Into the Unavailability of Active Warnings at Railway Level Crossings

116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events.The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle.Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis.We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.Copyright

Validation of a Driving Simulator Study on Driver Behavior at Passive Rail Level Crossings

Even though crashes between trains and road users are rare events at railway level crossings, they are one of the major safety concerns for the Australian railway industry. Nearmiss events at level crossings occur more frequently, and can provide more information about factors leading to level crossing incidents. In this paper we introduce a video analytic approach for automatically detecting and localizing vehicles from cameras mounted on trains for detecting near- miss events. To detect and localize vehicles at level crossings we extract patches from an image and classify each patch for detecting vehicles. We developed a region proposals algorithm for generating patches, and we use a Convolutional Neural Network (CNN) for classifying each patch. To localize vehicles in images we combine the patches that are classified as vehicles according to their CNN scores and positions. We compared our system with the Deformable Part Models (DPM) and Regions with CNN features (R-CNN) object detectors. Experimental results on a railway dataset show that the recall rate of of our proposed system is 29% higher than what can be achieved with DPM or R-CNN detectors.

Low-Cost Railway Level Crossings: Breaking Down the Barriers

This paper discusses human factors issues of low cost railway level crossings in Australia. Several issues are discussed in this paper including safety at level railway crossings, human factors considerations associated with the unavailability of a warning device, and a conceptual model for how safety could be compromised at railway level crossings following prolonged or frequent unavailability. The current paper summarises and extends pertinent literature that must be considered for effective interventions to improve safety and to advance our theoretical understanding of human behaviour at level crossings. Although the results of our research are not presented, we describe our experimental approach to progress the current lack of knowledge in this area. In particular we highlight where we can improve previous research methodology (independent & dependent variables) when investigating right-side failure at level crossings, which can produce results with greater validity and meaning. Our research aims to quantify risk to motorists at level crossings following right-side failure using a Human Reliability Assessment (HRA) method, supported by data collected using an advanced driving simulator. This method aims to identify human error within tasks and task units identified as part of the task analysis process. It is anticipated that by modelling driver behaviour the current study will be able to quantify human reliability. Such a risk assessment for the impact of right-side failure at level crossings is currently absent in the literature. Therefore it is crucial to offer quantification of success and failure of this intricate system. The task analysis allows human error identification for the precursors to risky driving to be achieved. If task analysis is not employed the error reduction method may be unsuitable and eventually unsuccessful. Our aim is also to determine those contexts that allow the system to operate successfully with the smallest probability of human error. Human behaviour during complex tasks such as driving through a level crossing is fundamentally context bound. Therefore this study also aims to quantify those performance-shaping factors that may contribute to vehicle train collisions by highlighting changes in the task units and driver physiology. Finally we consider a number of variables germane to ensuring external validity of our results. Without this inclusion, such an analysis could seriously underestimate risk.

Modelling risk at low-exposure railway level crossings: Supporting an argument for low-cost level crossing warning devices with lower levels of safety integrity

Objective: The behavioral validation of an advanced driving simulator for its use in evaluating passive level crossing countermeasures was performed for stopping compliance and speed profile. Background: Despite the fact that most research on emerging interventions for improving level crossing safety is conducted in a driving simulator, no study has validated the use of a simulator for this type of research. Method: We monitored driver behavior at a selected passive level crossing in the Brisbane region in Australia for 3 months (N = 916). The level crossing was then replicated in an advanced driving simulator, and we familiarized participant drivers (N = 54) with traversing this crossing, characterized by low road and rail traffic. Results: We established relative validity for the stopping compliance and the approach speed. Conclusion: This validation study suggests that driving simulators are an appropriate tool to study the effects of interventions at passive level crossing with low road and rail traffic, which are prone to reduced compliance due to familiarity. Application: This study also provides support for the findings of previous driving simulator studies conducted to evaluate compliance and approach speeds of passive level crossings.

Signal authentication and integrity schemes for next generation global navigation satellite systems

Level crossing risk continues to be a significant safety concern for the security of rail operations around the world. Over the last decade or so, a third of railway related fatalities occurred as a direct result of collisions between road and rail vehicles in Australia. Importantly, nearly half of these collisions occurred at railway level crossings with no active protection, such as flashing lights or boom barriers. Current practice is to upgrade level crossings that have no active protection. However, the total number of level crossings found across Australia exceed 23,500, and targeting the proportion of these that are considered high risk (e.g. public crossings with passive controls) would cost in excess of AU