Maritime autonomous surface ships: can we learn from unmanned aerial vehicle incidents using the perceptual cycle model?

Interest in Maritime Autonomous Surface Ships (MASS) is increasing as it is predicted that they can bring improved safety, performance and operational capabilities. However, their introduction is associated with a number of enduring Human Factors challenges (e.g.

Resilience engineering on the road: Using operator event sequence diagrams and system failure analysis to enhance cyclist and vehicle interactions.

Future visions of transport systems include both a drive towards automated vehicles and the need for sustainable, active, modes of travel. The combination of these requirements needs careful consideration to ensure the integration of automated vehicles does not compromise vulnerable road users. Transport networks need to be resilient to automation integration, which requires foresight of possible challenges in their interaction with other road users.

25 Years of road safety: The journey from thinking humans to systems-thinking.

Research into road safety has evolved from individual level component analysis to a much broader, systemic approach that acknowledges the fusion of 'socio' and 'technical' system elements. Over the past four decades, Professor Neville Stanton has contributed to over 179 journal articles, book chapters and conference papers in the field of road safety. The journey from 'thinking humans' to 'systems thinking' is demonstrated in this paper through the novel application of the Risk Management Framework (RMF) to the categorisation of research activities.

Predicting Design-Induced Error on the Flight Deck : An Aircraft Engine Oil Leak Scenario.

Objective: To explore the types of errors that commercial pilots may make when trying to resolve a suspected engine oil leak using the interfaces currently available.

Background: The decisions that pilots make often have to be made quickly and under time pressure, with the emphasis on avoiding critical situations from arising. To make the correct decisions, it is vital that pilots have accurate and up-to-date information available.

Who is responsible for automated driving? A macro-level insight into automated driving in the United Kingdom using the Risk Management Framework and Social Network Analysis.

To date, vehicle manufacturers have largely been left to their own initiatives when it comes to the design, development and implementation of automated driving features. Whilst this has enabled developments within the field to accelerate at a rapid pace, we are also now beginning to see the negative aspects of automated design (e.g.

Distributed Cognition on the road: Using EAST to explore future road transportation systems.

Connected and Autonomous Vehicles (CAV) are set to revolutionise the way in which we use our transportation system. However, we do not fully understand how the integration of wireless and autonomous technology into the road transportation network affects overall network dynamism. This paper uses the theoretical principles underlying Distributed Cognition to explore the dependencies and interdependencies that exist between system agents located within the road environment, traffic management centres and other external agencies in both non-connected and connected transportation systems.

Is partially automated driving a bad idea? Observations from an on-road study.

The automation of longitudinal and lateral control has enabled drivers to become "hands and feet free" but they are required to remain in an active monitoring state with a requirement to resume manual control if required. This represents the single largest allocation of system function problem with vehicle automation as the literature suggests that humans are notoriously inefficient at completing prolonged monitoring tasks. To further explore whether partially automated driving solutions can appropriately support the driver in completing their new monitoring role, video observations were collected as part of an on-road study using a Tesla Model S being operated in Autopilot mode.

Driver-centred vehicle automation: using network analysis for agent-based modelling of the driver in highly automated driving systems.

To the average driver, the concept of automation in driving infers that they can become completely 'hands and feet free'. This is a common misconception, however, one that has been shown through the application of Network Analysis to new Cruise Assist technologies that may feature on our roads by 2020. Through the adoption of a Systems Theoretic approach, this paper introduces the concept of driver-initiated automation which reflects the role of the driver in highly automated driving systems.

Keep the driver in control: Automating automobiles of the future.

Automated automobiles will be on our roads within the next decade but the role of the driver has not yet been formerly recognised or designed. Rather, the driver is often left in a passive monitoring role until they are required to reclaim control from the vehicle. This research aimed to test the idea of driver-initiated automation, in which the automation offers decision support that can be either accepted or ignored.

Contrasting models of driver behaviour in emergencies using retrospective verbalisations and network analysis.

Unlabelled: Automated assistance in driving emergencies aims to improve the safety of our roads by avoiding or mitigating the effects of accidents. However, the behavioural implications of such systems remain unknown. This paper introduces the driver decision-making in emergencies (DDMiEs) framework to investigate how the level and type of automation may affect driver decision-making and subsequent responses to critical braking events using network analysis to interrogate retrospective verbalisations.

What the drivers do and do not tell you: using verbal protocol analysis to investigate driver behaviour in emergency situations.

Although task analysis of pedestrian detection can provide us with useful insights into how a driver may behave in emergency situations, the cognitive elements of driver decision-making are less well understood. To assist in the design of future Advanced Driver Assistance Systems, such as Autonomous Emergency Brake systems, it is essential that the cognitive elements of the driving task are better understood. This paper uses verbal protocol analysis in an exploratory fashion to uncover the thought processes underlying behavioural outcomes represented by hard data collected using the Southampton University Driving Simulator.