Transport safety and human factors in the era of automation: What can transport modes learn from each other?

A Review of Human Performance Models for Prediction of Driver Behavior and Interactions With In-Vehicle Technology

OBJECTIVE

This study investigated the use of human performance modeling (HPM) approach for prediction of driver behavior and interactions with in-vehicle technology.

BACKGROUND

HPM has been applied in numerous human factors domains such as surface transportation as it can quantify and predict human performance; however, there has been no integrated literature review for predicting driver behavior and interactions with in-vehicle technology in terms of the characteristics of methods used and variables explored.

METHOD

A systematic literature review was conducted using Compendex, Web of Science, and Google Scholar. As a result, 100 studies met the inclusion criteria and were reviewed by the authors. Model characteristics and variables were summarized to identify the research gaps and to provide a lookup table to select an appropriate method.

RESULTS

The findings provided information on how to select an appropriate HPM based on a combination of independent and dependent variables. The review also summarized the characteristics, limitations, applications, modeling tools, and theoretical bases of the major HPMs.

CONCLUSION

The study provided a summary of state-of-the-art on the use of HPM to model driver behavior and use of in-vehicle technology. We provided a table that can assist researchers to find an appropriate modeling approach based on the study independent and dependent variables.

APPLICATION

The findings of this study can facilitate the use of HPM in surface transportation and reduce the learning time for researchers especially those with limited modeling background.

Managing the risks associated with technological disruption in the road transport system: a control structure modelling approach

Road transport is experiencing disruptive change from new first-of-a-kind technologies. While such technologies offer safety and operational benefits, they also pose new risks. It is critical to proactively identify risks during the design, development and testing of new technologies. The Systems Theoretic Accident Model and Processes (STAMP) method analyses the dynamic structure in place to manage safety risks. This study applied STAMP to develop a control structure model for emerging technologies in the Australian road transport system and identified control gaps. The control structure shows the actors responsible for managing risks associated with first-of-a-kind technologies and the existing control and feedback mechanisms. Gaps identified related to controls (e.g. legislation) and feedback mechanisms (e.g. monitoring for behavioural adaptation). The study provides an example of how STAMP can be used to identify control structure gaps requiring attention to support the safe introduction of new technologies.

A Dynamic Bayesian Network model to evaluate the availability of machinery systems in Maritime Autonomous Surface Ships

With their complex structure, multiple failure modes and lack of maintenance crew, the safety problem of Maritime Autonomous Surface Ships' (MASS) machinery systems are becoming an important research topic. The present study presents an availability model for ship machinery systems incorporating a maintenance strategy based on Dynamic Bayesian Networks (DBN). First, the availability of conventional ship machinery systems is evaluated and used as a benchmark based on the configuration and planned maintenance strategy. Secondly, the availability of MASS machinery systems is compared to the benchmark, before the introduction of any changes to the ship's configuration and planned maintenance strategy. Finally, the availability improvement strategies, including redundant designs and planned maintenance strategies at port, are proposed based on sensitivity analysis and planned maintenance cost minimization. To exemplify the model's application, a case study of a cooling water system is explored. Based on a sensitivity analysis using the model, it is possible to decide which components need to be redundant. Different redundancy designs and corresponding planned maintenance strategies can be adopted to meet the availability demand. It is also shown that redundancy and enhanced detection capabilities reduce much of the planned maintenance cost. This framework can be used in the early design stages to determine whether the MASS machinery systems' availability is at least equivalent to that of conventional ships, and has certain reference significance for redundant configuration designs and MASS planned maintenance strategy schedule.

Automation complacency on the road

Given that automation complacency, a hitherto controversial concept, is already used to blame and punish human drivers in current accident investigations and courts, it is essential to map complacency research in driving automation and determine whether current research can support its legitimate usage in these practical fields. Here, we reviewed its status quo in the domain and conducted a thematic analysis. We then discussed five fundamental challenges that might undermine its scientific legitimation: conceptual confusion exists in whether it is an individual versus systems problem; uncertainties exist in current evidence of complacency; valid measures specific to complacency are lacking; short-term laboratory experiments cannot address the long-term nature of complacency and thus their findings may lack external validity; and no effective interventions directly target complacency prevention. The Human Factors/Ergonomics community has a responsibility to minimise its usage and defend human drivers who rely on automation that is far from perfect.Practitioner summary: Human drivers are accused of complacency and overreliance on driving automation in accident investigations and courts. Our review work shows that current academic research in the driving automation domain cannot support its legitimate usage in these practical fields. Its misuse will create a new form of consumer harms.

Towards common ethical and safe 'behaviour' standards for automated vehicles

Automated vehicles (AVs) aim to dramatically improve traffic safety by reducing or eliminating human error, which remains the leading cause of road crashes. However, commonly accepted standards for the 'safe driving behaviour of machines' are pending and urgently needed. Unless a common understanding of safety as a design value is achieved, different manufacturers' driving styles may emerge, resulting in inconsistent, unpredictable and potentially unsafe 'behaviour' of AVs in certain situations. This paper aims to explore the main gaps and challenges towards establishing shared safety standards for the 'behaviour' of AVs, and contribute to their responsible traffic integration, by reviewing the state-of-the-art on AV safety in the core relevant disciplines: ethics of technology, safety science (engineering & human factors), and standardisation. The ethical and safety aspects investigated include the users' perception of AV safety, the ethical trade-offs in critical decision-making contexts, the pertinence of data-driven approaches for AVs to mimic human behaviour, and the responsibilities of various actors. Moreover, the paper reviews the current safety patterns, metrics (surrogate measures of safety - SMoS) and their thresholds introduced in existing research for three use cases: mixed traffic of AV and conventional vehicles, AV interaction with pedestrians and cyclists, and transition of control from machine to human driver. The results reveal several knowledge gaps within each discipline and highlights the lack of common understanding of safety across disciplines. On the basis of the results, the paper proposes a framework for further research on AV safety, identifying concrete opportunities for interdisciplinary research, with common goals and methodologies, and explicitly indicating the path for transfer of knowledge between sectors.

Human, Organisational and Societal Factors in Robotic Rail Infrastructure Maintenance

Robotics are set to play a significant role in the maintenance of rail infrastructure. However, the introduction of robotics in this environment requires new ways of working for individuals, teams and organisations and needs to reflect societal attitudes if it is to achieve sustainable goals. The following paper presents a qualitative analysis of interviews with 25 experts from rail and robotics to outline the human and organisational issues of robotics in the rail infrastructure environment. Themes were structured around user, team, organisational and societal issues. While the results point to many of the expected issues of robotics (trust, acceptance, business change), a number of issues were identified that were specific to rail. Examples include the importance of considering the whole maintenance task lifecycle, conceptualizing robotic teamworking within the structures of rail maintenance worksites, the complex upstream (robotics suppliers) and downstream (third-party maintenance contractors) supply chain implications of robotic deployment and the public acceptance of robotics in an environment that often comes into direct contact with passenger and people around the railways. Recommendations are made in the paper for successful, human-centric rail robotics deployment.

cardiac autonomic control and neural arousal as indexes of fatigue in professional bus drivers

Background

Bus driving is a mentally demanding activity that requires prolonged attention to ensure safety. The aim of the study was to assess mental fatigue caused by driving a public bus and to find a profile of workers at higher risk.

Methods

We evaluated changes of critical flicker fusion (CFF) (index of central arousal) and heart rate variability (HRV) (index of autonomic balance) in a 6-hour driving shift on a real route, in 31 professional bus drivers, and we tested the influence of personal factors such as sleep quality, BMI, and age. Paired t-test was used to test differences of CFF and HRV between both initial and final phase of driving, while multiple linear regression tested the influence of personal variables on the indexes of mental fatigue.

Results

Results showed that CFF significantly decreased after 6 hours of bus driving (41.91 Hz, sd 3.31 vs. 41.15 Hz, sd 3.15; p = 0.041), and heart rate significantly decreased in the final phase of driving, with respect to the initial phase (85 vs. 78 bpm, p = 0.027). Increasing age (beta = -0.729, p = 0.022), risk of obstructive sleep apnea syndrome (beta = -0.530, p = 0.04), and diurnal sleepiness (beta = -0.406, p = 0.017) showed a significant effect on influencing mental fatigue.

Conclusion

Elderly drivers at higher risk of sleep disorders are more prone to mental fatigue, when exposed to driving activity. Monitoring indexes of central arousal and autonomic balance, coupled with the use of structured questionnaires can represent a useful strategy to detect profile of workers at higher risk of mental fatigue in such duty.

Situation Awareness Measurement in Remotely Controlled Cars

This study reviews the current information concerning the measurement of the situation awareness (SA) of the teleoperated drivers of remotely controlled cars. The teleoperated drivers who drive these cars are in a remote location, and they control the cars through a communication interface. The objective methods with probes are beneficial in measuring SA on a closed circuit without real traffic. Questions specifically should address the information provided on the road by haptic sensations, such as the slope of the road and the vehicle's speed. Methods for measuring SA that involve probes and interruptions obviously are not suitable for use on public roads. A stable environment for the display and control of the communication interface is suitable for an eye tracker in measuring SA. These features also facilitate the use of subjective observer-rating methods. Both of these methods are suitable for driving on real roads because they are not intrusive. SA research in a real-road environment also should demonstrate how the SA of other drivers is affected by seeing a car without a driver. Given the remote character of driving, cultural differences in cognition may have a significant influence on the SA of the teleoperated driver.