Sustainability, transport and design: reviewing the prospects for safely encouraging eco-driving

Private vehicle use contributes a disproportionately large amount to the degradation of the environment we inhabit. Technological advancement is of course critical to the mitigation of climate change, however alone it will not suffice; we must also see behavioural change. This paper will argue for the application of Ergonomics to the design of private vehicles, particularly low-carbon vehicles (e.g. hybrid and electric), to encourage this behavioural change. A brief review of literature is offered concerning the effect of the design of a technological object on behaviour, the inter-related nature of goals and feedback in guiding performance, the effect on fuel economy of different driving styles, and the various challenges brought by hybrid and electric vehicles, including range anxiety, workload and distraction, complexity, and novelty. This is followed by a discussion on the potential applicability of a particular design framework, namely Ecological Interface Design, to the design of in-vehicle interfaces that encourage energy-conserving driving behaviours whilst minimising distraction and workload, thus ensuring safety

Computational driver behavior models for vehicle safety applications

The aim of this thesis is to investigate how human driving behaviors can be formally described in mathematical models intended for online personalization of advanced driver assistance systems (ADAS) or offline virtual safety evaluations. Both longitudinal (braking) and lateral (steering) behaviors in routine driving and emergencies are addressed. Special attention is paid to driver glance behavior in critical situations and the role of peripheral vision.First, a hybrid framework based on autoregressive models with exogenous input (ARX-models) is employed to predict and classify driver control in real time. Two models are suggested, one targeting steering behavior and the other longitudinal control behavior. Although the predictive performance is unsatisfactory, both models can distinguish between different driving styles.Moreover, a basic model for drivers\u27 brake initiation and modulation in critical longitudinal situations (specifically for rear-end conflicts) is constructed. The model is based on a conceptual framework of noisy evidence accumulation and predictive processing. Several model extensions related to gaze behavior are also proposed and successfully fitted to real-world crashes and near-crashes. The influence of gaze direction is further explored in a driving simulator study, showing glance response times to be independent of the glance\u27s visual eccentricity, while brake response times increase for larger gaze angles, as does the rate of missed target detections.Finally, the potential of a set of metrics to quantify subjectively perceived risk in lane departure situations to explain drivers\u27 recovery steering maneuvers was investigated. The most influential factors were the relative yaw angle and splay angle error at steering initiation. Surprisingly, it was observed that drivers often initiated the recovery steering maneuver while looking off-road.To sum up, the proposed models in this thesis facilitate the development of personalized ADASs and contribute to trustworthy virtual evaluations of current, future, and conceptual safety systems. The insights and ideas contribute to an enhanced, human-centric system development, verification, and validation process. In the long term, this will likely lead to improved vehicle safety and a reduced number of severe injuries and fatalities in traffic

SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving

Autonomous driving confronts great challenges in complex traffic scenarios, where the risk of Safety of the Intended Functionality (SOTIF) can be triggered by the dynamic operational environment and system insufficiencies. The SOTIF risk is reflected not only intuitively in the collision risk with objects outside the autonomous vehicles (AVs), but also inherently in the performance limitation risk of the implemented algorithms themselves. How to minimize the SOTIF risk for autonomous driving is currently a critical, difficult, and unresolved issue. Therefore, this paper proposes the "Self-Surveillance and Self-Adaption System" as a systematic approach to online minimize the SOTIF risk, which aims to provide a systematic solution for monitoring, quantification, and mitigation of inherent and external risks. The core of this system is the risk monitoring of the implemented artificial intelligence algorithms within the AV. As a demonstration of the Self-Surveillance and Self-Adaption System, the risk monitoring of the perception algorithm, i.e., YOLOv5 is highlighted. Moreover, the inherent perception algorithm risk and external collision risk are jointly quantified via SOTIF entropy, which is then propagated downstream to the decision-making module and mitigated. Finally, several challenging scenarios are demonstrated, and the Hardware-in-the-Loop experiments are conducted to verify the efficiency and effectiveness of the system. The results demonstrate that the Self-Surveillance and Self-Adaption System enables dependable online monitoring, quantification, and mitigation of SOTIF risk in real-time critical traffic environments.Comment: 16 pages, 10 figures, 2 tables, submitted to IEEE TIT

Risk-taking and expenditure in digital roulette: Examining the impact of tailored dynamic information and warnings on gambling attitudes and behaviours.

Digital gambling is the fastest growing form of gambling in the world (Reilly & Smith, 2013a). Technological advancements continually increase access to gambling, which has led to increased social acceptance and uptake (Dragicevic & Tsogas, 2014) with Roulette being among the most popular games played both online and on Electronic Gaming Machines. In response, gambling stakeholders have drawn on the structural characteristics of gambling platforms to develop and improve Responsible Gambling (RG) devices for casual gamblers. Many RG data-tracking systems employ intuitive ‘traffic-light’ metaphors that enable gamblers to monitor their gambling (e.g. Wood & Griffiths, 2008), though uptake of voluntary RG devices is low (Schellinck & Schrans, 2011), leading to calls for mandatory RG systems. Another area that has received considerable RG research focus involves the use of pop-up messages (Auer & Griffiths, 2014). Studies have examined various message content, such as correcting erroneous beliefs, encouraging self-appraisal, gambling cessation, and the provision of personalised feedback. To date, findings have been inconsistent but promising. A shift towards the use of personalised information has become the preferred RG strategy, though message content and timing/frequency requires improvement (Griffiths, 2014). Moreover, warning messages are unable to provide continuous feedback to gamblers. In response to this, and calls for a ‘risk meter’ to improve monitoring of gambling behaviours (Wiebe & Philander, 2013), this thesis tested the impact of a risk meter alongside improved pop-up warning messages as RG devices for within-session roulette gambling. The thesis aimed to establish the optimal application of these devices for facilitating safer gambling behaviours. In support of the aims of RG research to evaluate the impact of devices on gambling attitudes and behaviours, the Elaboration Likelihood Model was identified as a suitable framework to test the proposed RG devices (Petty & Cacioppo, 1986). Both the interactive risk meter and pop-up messages were developed based on existing methods and recommendations in the RG literature, and examined via a series of laboratory-based roulette simulation experiments. Overall, results found the risk meter to be most effective when used as an interactive probability meter. Self-appraisal/Informative pop-up warnings were examined alongside expenditure-specific and hyrbid warnings. Findings showed that hybrid messages containing both types of information to be most effective, with optimal display points at 75%, 50%, 25% and 10% of remaining gambling credit. The final study tested both optimised devices (probability meter and hybrid messages). Results showed that using both RG devices in combination was most effective in facilitating reduced gambling risk and early within-session gambling cessation. Findings support the use of personalised, interactive RG devices using accurate context-specific information for the facilitation of safer gambling. The ELM was shown to be an effective model for testing RG devices, though findings suggested only temporary shifts in attitude change and a lack of impact on future gambling intentions. Overall, support for the implementation of RG devices that facilitate positive, temporary behaviour change that do not negatively impact on broader gambling attitudes or gambling enjoyment. Implications for theory, implementation, and RG frameworks are discussed, alongside recommendations for future research.n/