Victoria A. Banks

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. The test case examined a combination of lateral and longitudinal control in addition to an auto-overtake system. Despite putting the driver in control of the automated systems by enabling them to accept or ignore behavioural suggestions (e.g. overtake), there were still issues associated with increased workload and decreased trust. These issues are likely to have arisen due to the way in which the automated system has been designed. Recommendations for improvements in systems design have been made which are likely to improve trust and make the role of the driver more transparent concerning their authority over the automated system.

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. Practitioner Summary: This research assessed the appropriateness of verbal protocol analysis (VPA) in investigating driver behaviour in addition to quantitative data collected using Southampton Universitys Driving Simulator. VPA proved to be a useful extension tool in validating and enhancing hard data. A number of practical recommendations have been offered to guide future research.

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

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. Four DDMiE models were constructed to represent different levels of automation within the driving task and its effects on driver decision-making. Findings suggest that whilst automation does not alter the decision-making pathway (e.g. the processes between hazard detection and response remain similar), it does appear to significantly weaken the links between information-processing nodes. This reflects an unintended yet emergent property within the task network that could mean that we may not be improving safety in the way we expect. Practitioner Summary: This paper contrasts models of driver decision-making in emergencies at varying levels of automation using the Southampton University Driving Simulator. Network analysis of retrospective verbalisations indicates that increasing the level of automation in driving emergencies weakens the link between information-processing nodes essential for effective decision-making.

Transition to manual: comparing simulator with on-road control transitions

BACKGROUND Whilst previous research has explored how driver behaviour in simulators may transfer to the open road, there has been relatively little research showing the same transfer within the field of driving automation. As a consequence, most research into human-automation interaction has primarily been carried out in a research laboratory or on closed-circuit test tracks. OBJECTIVE The aim of this study was to assess whether research into non-critical control transactions in highly automated vehicles performed in driving simulators correlate with road driving conditions. METHOD Twenty six drivers drove a highway scenario using an automated driving mode in the simulator and twelve drivers drove on a public motorway in a Tesla Model S with the Autopilot activated. Drivers were asked to relinquish, or resume control from the automation when prompted by the vehicle interface in both the simulator and on road condition. RESULTS Drivers were generally faster to resume control in the on-road driving condition. However, strong positive correlations were found between the simulator and on road driving conditions for drivers transferring control to and from automation. No significant differences were found with regard to workload, perceived usefulness and satisfaction between the simulator and on-road drives. CONCLUSION The results indicate high levels of relative validity of driving simulators as a research tool for automated driving research.

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

Abstract 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. Using a combination of traditional task analysis and the application of quantitative network metrics, this agent-based modelling paper shows how the role of the driver remains an integral part of the driving system implicating the need for designers to ensure they are provided with the tools necessary to remain actively in-the-loop despite giving increasing opportunities to delegate their control to the automated subsystems. Practitioner Summary: This paper describes and analyses a driver-initiated command and control system of automation using representations afforded by task and social networks to understand how drivers remain actively involved in the task. A network analysis of different driver commands suggests that such a strategy does maintain the driver in the control loop.

What the crash dummies don't tell you: The interaction between driver and automation in emergency situations

Systems design is plagued by criticism for failing to adequately define the role of the human within the system as a whole. Autonomous Emergency Braking (AEB) systems automate elements of the driving task by warning the driver of a collision risk and, if necessary, applying the brakes to reduce collision impact. As with all automated technologies, questions remain over whether or not AEB fundamentally changes the driving task by affecting the ways in which the driver interacts with vehicle systems. In order to address these concerns, Operator Sequence Diagrams have been developed to provide an insight into the roles of the driver and vehicle sub-systems in an emergency situation using the distributed cognition approach.

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. This represents a significant step forward in modelling complex sociotechnical systems as it shows that the principles underlying Distributed Cognition can be applied to macro-level systems using the visual representations afforded by the Event Analysis of Systemic Teamwork (EAST) method.

The Unknown Paradox of “Stop the Crash” Systems: Are We Really Improving Driver Safety?

This research assessed the appropriateness of the Autonomous Emergency Brake systems design using the Southampton University Driving Simulator. A total of 48 participants drove along a test route simulating testing procedures for Pedestrian Protection Systems at different levels of automation using different design strategies. It was found that whilst improvements to overall road safety was undeniably great regardless of design strategy, drivervehicle interaction patterns were affected in unexpected ways depending upon method of implementation. Contrasting design principles can therefore have varying effects on driver responses meaning that despite significant reductions in accident involvement, safety may not be improving in the way we expect. Overall, the paper concludes that we may be altering normal driver-vehicle interactions in a way that could be detrimental to driver behaviour in emergency situations opening up the debate over whether or not the benefits of automation outweigh potential costs.

Keeping the Driver in the Loop: The ‘Other’ Ethics of Automation

Automated vehicles are expected to revolutionise everyday travel with anticipated benefits of improved road safety, comfort and mobility. However, they also raise complex ethical challenges. Ethical debates have primarily centred around moral judgements that must be made by autonomous vehicles in safety-critical situations, with proposed solutions typically based on deontological principles or consequentialism. However, ethics should also be acknowledged in the design, development and deployment of partially-automated systems that invariably rely upon the human driver to monitor and intervene when required, even though they may be ill-prepared to do so. In this literature review, we explore the lesser-discussed ethics associated with the role of, and expectations placed upon, the human driver in partially-automated vehicles, discussing factors such as the marketing and deployment of these vehicles, and the impact upon the human driver’s development of trust and complacency in automated functionality, concluding that the human driver must be kept ‘in the loop’ at all times.

Automobile Automation : Distributed Cognition on the Road

Increasing levels of driving automation has changed the role of the driver from active operator to passive monitor. However, Systems Design has been plagued by criticism for failing to acknowledge the new role of the driver within the system network. To understand the driver’s new role within an automated driving system, the theory of Distributed Cognition is adopted. This approach provides a useful framework for the investigation of allocation of function between multiple agents in the driving system. A Systems Design Framework has been developed that outlines how the Distributed Cognition paradigm can be applied to driving using both qualitative and quantitative research methodologies.