ACC design for safety and fuel efficiency: the acceptance of safety margins when adopting different driving styles

For adaptive cruise control (ACC) systems to be accepted and used safely, the transitions from cruise control mode to necessary driver intervention need to be obvious to the driver. Previous research shows that drivers have natural boundaries for acceptable values for time headway and time to collision to a car in front, which define at what point they are likely to step on the brake pedal. These boundaries can define an intuitive limit for ACC engagement. However, such boundaries may not be the same for all drivers, and not even for the same driver, whose goals may vary. The present research aimed to measure mental model boundaries in the context of different goals with a motorway cut-in scenario in a driving simulator. Participants drove in three conditions, after being asked to ‘drive safely, ‘drive fuel-efficiently’ and after no specific instructions. The results show that both the safe and eco-driving instructions led drivers to brake at longer safety margins. These findings indicate that, as drivers follow different goals, e.g. as they are reminded to drive safely or eco-friendly, their preferences for operational limits of ACCs may change. This needs to be taken into account for design decisions, e.g. using ‘safe’ and ‘eco’ modes when driving

The exact determination of subjective risk and comfort thresholds in car following

In this study the location of vehicle to vehicle distance thresholds for self-reported subjective risk and comfort was researched. Participants were presented with ascending and descending time headway sequences in a driving simulator. This so called method of limits of ascending and descending stimuli (Gouy, Diels, Reed, Stevens, & Burnett, 2012) was refined to efficiently determine individual thresholds for stable time headways with a granularity of 0.1 seconds. Time headway thresholds were researched for 50, 100, and 150 km/h in a city, rural, and highway setting. Furthermore, thresholds for self-driving (level 0 automation: NHTSA, 2013) were compared with thresholds for the experience of subjective risk and comfort in assisted driving, similar to adaptive cruise control (level 1 automation). Results show that preferred individual time headways vary between subjects. Within subjects however, time headway thresholds do not significantly differ for different speeds. Furthermore we found that there was no significant difference between time headways of self-driving and distance-assisted driving. The relevance of these findings for the development of adaptive cruise control systems, autonomous driving and driver behavior modelling is discussed

The potential mental health effects of remote control in an autonomous maritime world

Many maritime activities, such as loading, unloading and transporting cargoes, consist primarily of long periods of low-stress, with some moments of high stress during complex manoeuvres or unanticipated, dangerous, incidences. The increase in autonomy provided by machines and AI is beginning to take over certain tasks in the maritime sector, to reduce costs and mitigate human error. However, with the current levels of autonomous technology available, legislation, and public trust in the technology, such solutions are only able to remove majority of tasks associated with low-stress periods. In fact, many current remote control solutions still suggest relying on human operators to deal with the complex situations AI struggle with. Such a human–automation relationship could endanger the human element. The concern is that, if the human user is spending a disproportionate part of their time dealing with multiple, unconnected, high-stress tasks, without periods to de-stress, this could increasingly put workers at risk. This paper seeks to highlight potential technical, social, and mental, issues that may arise as the sector begins implementing semi-autonomous and fully autonomous maritime operations

Human factors : a new approach for designing the truck-driver system

The logistics sector is an often forgotten force behind modern life in the UK, and it is increasingly under pressure to become more efficient, more safety-conscious, and more environmentally sustainable. This triple bottom line necessitates deep changes to the traditional way of working. As evidenced by an expert-led technology forecast, many technological and organisational interventions are on the horizon for the next 15-30 years. This rapid pace of advancement, together with the frequent assumption that workers are ‘hyper-rational’, echoes a worrying pattern from other sectors that have since benefited from human factors & ergonomics (HF/E) expertise. This thesis aims to apply HF/E principles and methods to both current and projected future truck-driver scenarios, in order to leverage the most agile and intelligent agent in the logistics system: the human. Despite a lack of past work at this intersection, logistics and HF/E can be drawn together by their mutual use of systems complexity concepts. This thesis proposes that logistics is a large, complex adaptive socio-technical system (CASTS), and reviews HF/E methods to determine their fit to different system scales and dynamics. From this it is determined that initial work requires a bottom-up focus on the truck-driver system. A range of methods are employed to understand the existing truck driving task and what it requires of the modern driver; identify and prioritise potentially critical system ‘parts’; design new supportive technologies from scratch in a way that allows for emergent behaviour; and analytically prototype how truck-driver systems are likely to change in projected future scenarios. This work provides new practical insights for current truck-driver systems, and a map of how this may change – shedding light on potential future problems and how we might adapt to them before they occur. Not only does this thesis provide a solid empirical foundation and a ‘direction of travel’, it also contributes the methodological guidance necessary to strategise next steps beyond this thesis, into deeper logistics complexity. Taken together this demonstrates the power of human factors methods for logistics, and their potential for other unexplored ‘complex adaptive sociotechnical systems’ (CASTS)