Theoretical perspectives on motorcycle training: reducing risk taking

Little past empirical support has been found for the efficacy of motorcycle rider training as a road safety countermeasure. However, it has been argued that rider training should focus more particularly on the psychosocial factors that influence risk taking behaviour in addition to the traditional practice of developing vehicle-handling skills. This paper examines how rider training to reduce risk taking could be guided by appropriate theories. Two fundamental perspectives are examined: firstly training can be considered in terms of behaviour change, and secondly in terms of adult learning. Whilst behaviour change theories assume some pre-existing level of dysfunctional behaviour, an adult learning perspective does not necessarily carry this assumption. This distinction in perspectives conceptually aligns with the notions of intervention and prevention (respectively), with possible implications for specific target groups for pre-licence and post-licence training. The application of the Theory of Reasoned Action (Ajzen & Fishbein, 1975, 1980) and Transformative Learning Theory (Mezirow, 1997) to a pre-licence rider training program in Queensland, Australia is discussed.

Measuring the effect of motorcycle rider training on psychosocial influences for risk taking

Aim: Whilst motorcycle rider training is commonly incorporated into licensing programs in many developed nations, little empirical support has been found in previous research to prescribe it as an effective road safety countermeasure. It has been posited that the lack of effect of motorcycle rider training on crash reduction may, in part, be due to the predominant focus on skills-based training with little attention devoted to addressing attitudes and motives that influence subsequent risky riding. However, little past research has actually endeavoured to measure attitudinal and motivational factors as a function of rider training. Accordingly, this study was undertaken to assess the effect of a commercial motorcycle rider training program on psychosocial factors that have been shown to influence risk taking by motorcyclists. Method: Four hundred and thirty-eight motorcycle riders attending a competency-based licence training course in Brisbane, Australia, voluntarily participated in the study. A self-report questionnaire adapted from the Rider Risk Assessment Measure (RRAM) was administered to participants at the commencement of training, then again at the conclusion of training. Participants were informed of the independent nature of the research and that their responses would in no way effect their chance of obtaining a licence. To minimise potential demand characteristics, participants were instructed to seal completed questionnaires in envelopes and place them in a sealed box accessible only by the research team (i.e. not able to be viewed by instructors). Results: Significant reductions in the propensity for thrill seeking and intentions to engage in risky riding in the next 12 months were found at the end of training. In addition, a significant increase in attitudes to safety was found. Conclusions: These findings indicate that rider training may have a positive short-term influence on riders’ propensity for risk taking. However, such findings must be interpreted with caution in regard to the subsequent safety of riders as these factors may be subject to further influence once riders are licensed and actively engage with peers during on-road riding. This highlights a challenge for road safety education / training programs in regard to the adoption of safety practices and the need for behavioural follow-up over time to ascertain long-term effects. This study was the initial phase of an ongoing program of research into rider training and risk taking framed around Theory of Planned Behaviour concepts. A subsequent 12 month follow-up of the study participants has been undertaken with data analysis pending.

Prisons, dietetic services, research and training : win win

Learning Outcome: Gain knowledge in the area of dietetic training in Australia and the benefits of collaborative partnerships between government and universities to achieve improvements in dietetic service delivery, evidenced based practice, and student placements. Prisoners have high rates of chronic disease, however dietetic services and research in this sector is limited. Securing high quality professional practice placements for dietetic training in Australia is competitive, and prisons provide exciting opportunities. Queensland University of Technology (QUT) has a unique twenty year partnership with Queensland Corrective Services (QCS) with a service learning model placing final year dietetic students within prisons. Building on this partnership, in 2007 a new joint position was funded to establish dietetic services to over 5500 prisoners and support viable best practice dietetic education. Evaluation of the past three years of this partnership has shown an expansion of QUT student placements in Queensland prisons, with a third of final year students each undertaking 120 hours of foodservice management practicum. Student evaluations of placement over this period are much higher than the University average. Through the joint position student projects have been targeted on strategic areas to support nutrition and dietetic policy and practice. Projects have been broadened from menu reviews to more comprehensive quality improvement and dietetic research activities, with all student learning activities transferrable to other foodservice settings. Student practice in the prisons has been extended beyond foodservice management to include group education and dietetic counseling. For QCS, student placements have equated to close to a full-time dietitian position, with nutrition policy now being implemented as an outcome of this support. This innovative partnership has achieved a sustainable student placement model, supported research, whilst delivering dietetic services to a difficult to access group. Funding Disclosure: None

Visual route recognition with a handful of bits

In this paper we use a sequence-based visual localization algorithm to reveal surprising answers to the question, how much visual information is actually needed to conduct effective navigation? The algorithm actively searches for the best local image matches within a sliding window of short route segments or 'sub-routes', and matches sub-routes by searching for coherent sequences of local image matches. In contract to many existing techniques, the technique requires no pre-training or camera parameter calibration. We compare the algorithm's performance to the state-of-the-art FAB-MAP 2.0 algorithm on a 70 km benchmark dataset. Performance matches or exceeds the state of the art feature-based localization technique using images as small as 4 pixels, fields of view reduced by a factor of 250, and pixel bit depths reduced to 2 bits. We present further results demonstrating the system localizing in an office environment with near 100% precision using two 7 bit Lego light sensors, as well as using 16 and 32 pixel images from a motorbike race and a mountain rally car stage. By demonstrating how little image information is required to achieve localization along a route, we hope to stimulate future 'low fidelity' approaches to visual navigation that complement probabilistic feature-based techniques.

Learning in and beyond small business advisory programmes

The purpose of this paper is to analyse how participants learn in small business advisory programmes and to explore the impact of these learning programmes on the development of reflective learning dispositions in participants. The research involves two case studies of small business advisory programmes in Queensland, a state of Australia. One involves training in the use of GPS/GIS technology amongst rural SMEs and the other seeks to develop improved management and operational capabilities in regional and metropolitan manufacturing SMEs. Face to face semi-structured interviews were conducted throughout rural, regional and metropolitan Queensland with participants, trainers and senior executives in the administering organisations that ran the programmes. Learning in these programmes occurs through a combination of interaction with others and the adoption of practice-based and learner-centred processes. The impact of the programmes on participants includes the development of reflective learning dispositions, improved confidence in learning and appreciation of the value of new knowledge to their business. The research suggests that small business training programmes have the potential to affect the development of critical reflective learning dispositions in participants which is of fundamental importance to the development of a learning or knowledge economy.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.