Methodology to assess safety effects of future Intelligent Transport Systems on railway level crossings

There is consistent evidence showing that driver behaviour contributes to crashes and near miss incidents at railway level crossings (RLXs). The development of emerging Vehicle-to-Vehicle and Vehicle-to-Infrastructure technologies is a highly promising approach to improve RLX safety. To date, research has not evaluated comprehensively the potential effects of such technologies on driving behaviour at RLXs. This paper presents an on-going research programme assessing the impacts of such new technologies on human factors and drivers’ situational awareness at RLX. Additionally, requirements for the design of such promising technologies and ways to display safety information to drivers were systematically reviewed. Finally, a methodology which comprehensively assesses the effects of in-vehicle and road-based interventions warning the driver of incoming trains at RLXs is discussed, with a focus on both benefits and potential negative behavioural adaptations. The methodology is designed for implementation in a driving simulator and covers compliance, control of the vehicle, distraction, mental workload and drivers’ acceptance. This study has the potential to provide a broad understanding of the effects of deploying new in-vehicle and road-based technologies at RLXs and hence inform policy makers on safety improvements planning for RLX

Producing a commentary slows concurrent hazard perception responses

Commentary driver training involves teaching drivers how to verbally acknowledge their perceptual and cognitive processes while driving, and has been shown to improve performance in driving-related tasks. However, those studies demonstrating benefits of commentary training have not done so under conditions of live commentary, which is the typical protocol used with advanced drivers. In the current study we present the results of two experiments that show that producing a commentary can actually slow responses to hazards on a concurrent hazard perception task. In Experiment 1 participants producing a live commentary showed significantly longer hazard response times than an untrained, silent, control group. In Experiment 2 a shorter, clipped commentary was introduced to attempt to reduce the demands placed upon participants. However, both the clipped and full commentary conditions showed reduced accuracy and longer response times, relative to a silent condition, and no difference was observed between the two types of commentary. Analysis of eye movements in both experiments revealed that fixation durations were shorter when a commentary was produced, but time to first fixate the hazard was not affected. This suggests that commentaries encourage more active interrogation of the visual scene, but that this can be detrimental to performance in average drivers

Integrating driving and traffic simulators for the study of railway level crossing safety interventions: a methodology

Safety at Railway Level Crossings (RLXs) is an important issue within the Australian transport system. Crashes at RLXs involving road vehicles in Australia are estimated to cost $10 million each year. Such crashes are mainly due to human factors; unintentional errors contribute to 46% of all fatal collisions and are far more common than deliberate violations. This suggests that innovative intervention targeting drivers are particularly promising to improve RLX safety. In recent years there has been a rapid development of a variety of affordable technologies which can be used to increase driver’s risk awareness around crossings. To date, no research has evaluated the potential effects of such technologies at RLXs in terms of safety, traffic and acceptance of the technology. Integrating driving and traffic simulations is a safe and affordable approach for evaluating these effects. This methodology will be implemented in a driving simulator, where we recreated realistic driving scenario with typical road environments and realistic traffic. This paper presents a methodology for evaluating comprehensively potential benefits and negative effects of such interventions: this methodology evaluates driver awareness at RLXs , driver distraction and workload when using the technology . Subjective assessment on perceived usefulness and ease of use of the technology is obtained from standard questionnaires. Driving simulation will provide a model of driving behaviour at RLXs which will be used to estimate the effects of such new technology on a road network featuring RLX for different market penetrations using a traffic simulation. This methodology can assist in evaluating future safety interventions at RLXs

User expectations of partial driving automation capabilities and their effect on information design preferences in the vehicle

Partially automated vehicles present interface design challenges in ensuring the driver remains alert should the vehicle need to hand back control at short notice, but without exposing the driver to cognitive overload. To date, little is known about driver expectations of partial driving automation and whether this affects the information they require inside the vehicle. Twenty-five participants were presented with five partially automated driving events in a driving simulator. After each event, a semi-structured interview was conducted. The interview data was coded and analysed using grounded theory. From the results, two groupings of driver expectations were identified: High Information Preference (HIP) and Low Information Preference (LIP) drivers; between these two groups the information preferences differed. LIP drivers did not want detailed information about the vehicle presented to them, but the definition of partial automation means that this kind of information is required for safe use. Hence, the results suggest careful thought as to how information is presented to them is required in order for LIP drivers to safely using partial driving automation. Conversely, HIP drivers wanted detailed information about the system's status and driving and were found to be more willing to work with the partial automation and its current limitations. It was evident that the drivers' expectations of the partial automation capability differed, and this affected their information preferences. Hence this study suggests that HMI designers must account for these differing expectations and preferences to create a safe, usable system that works for everyone. [Abstract copyright: Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

In loco intellegentia: Human factors for the future European train driver

The European Rail Traffic Management System (ERTMS) represents a step change in technology for rail operations in Europe. It comprises track-to-train communications and intelligent on-board systems providing an unprecedented degree of support to the train driver. ERTMS is designed to improve safety, capacity and performance, as well as facilitating interoperability across the European rail network. In many ways, particularly from the human factors perspective, ERTMS has parallels with automation concepts in the aviation and automotive industries. Lessons learned from both these industries are that such a technology raises a number of human factors issues associated with train driving and operations. The interaction amongst intelligent agents throughout the system must be effectively coordinated to ensure that the strategic benefits of ERTMS are realised. This paper discusses the psychology behind some of these key issues, such as Mental Workload (MWL), interface design, user information requirements, transitions and migration and communications. Relevant experience in aviation and vehicle automation is drawn upon to give an overview of the human factors challenges facing the UK rail industry in implementing ERTMS technology. By anticipating and defining these challenges before the technology is implemented, it is hoped that a proactive and structured programme of research can be planned to meet them

Does Familiarity breed inattention? Why drivers crash on the roads they know best

This paper describes our research into the nature of everyday driving, with a particular emphasis on the processes that govern driver behaviour in familiar, well - practiced situations. The research examined the development and maintenance of proceduralised driving habits in a high-fidelity driving simulator by paying 29 participants to drive a simulated road regularly over three months of testing. A range of measures, including detection task performance and driving performance were collected over the course of 20 sessions. Performance from a yoked control group who experienced the same road scenarios in a single session was also measured. The data showed the development of stereotyped driving patterns and changes in what drivers noticed, indicative of in attentional blindness and “driving without awareness”. Extended practice also resulted in increased sensitivity for detecting changes to foveal road features associated with vehicle guidance and performance on an embedded vehicle detection task (detection of a specific vehicle type). The changes in attentional focus and driving performance resulting from extended practice help explain why drivers are at increased risk of crashing on roads they know well. Identifying the features of familiar roads that attract driver attention, even when they are driving without awareness, can inform new interventions and designs for safer roads. The data also provide new light on a range of previous driver behaviour research including a “Tandem Model” that includes both explicit and implicit processes involved in driving performance

Understanding speeding in school zones in Malaysia and Australia using an extended Theory of Planned Behaviour: The potential role of mindfulness

Speeding in school zones is a problem in both Malaysia and Australia. While there are differences between the countries in terms of school zone treatments and more generally, these differences do not explain why people choose to speed in school zones. Because speeding is usually an intentional behaviour, the Theory of Planned Behaviour (TPB) has been used to understand speeding and develop interventions, however it has limitations which can be addressed by extending the model to incorporate other constructs. One promising construct is mindfulness, which can improve the explanatory value of the TPB by taking into account unintentional speeding attributable to a lack of focus on important elements of the driving environment. We explain what mindfulness is (and is not), how it can assist in providing a better understanding of speeding in school zones, and how it can contribute to the development of interventions. We then outline a program of research which has been commenced, investigating the contribution of mindfulness to an understanding of speed choice in school zones in two different settings (Australia and Malaysia) using the TPB

Novel Multimodal Feedback Techniques for In-Car Mid-Air Gesture Interaction

This paper presents an investigation into the effects of different feedback modalities on mid-air gesture interaction for infotainment systems in cars. Car crashes and near-crash events are most commonly caused by driver distraction. Mid-air interaction is a way of reducing driver distraction by reducing visual demand from infotainment. Despite a range of available modalities, feedback in mid-air gesture systems is generally provided through visual displays. We conducted a simulated driving study to investigate how different types of multimodal feedback can support in-air gestures. The effects of different feedback modalities on eye gaze behaviour, and the driving and gesturing tasks are considered. We found that feedback modality influenced gesturing behaviour. However, drivers corrected falsely executed gestures more often in non-visual conditions. Our findings show that non-visual feedback can reduce visual distraction significantl