An exploration of the maturity model concept as a vehicle for higher education institutions to assess their capability to address student engagement : a work in progress

Gaining a competitive edge in the area of the engagement, success and retention of commencing students is a significant issue in higher education, made more so currently because of the considerable and increasing pressure on teaching and learning from the new standards framework and performance funding. This paper introduces the concept of maturity models (MMs) and their application to assessing the capability of higher education institutions (HEIs) to address student engagement, success and retention (SESR). A concise description of the features of maturity models is presented with reference to an SESR-MM currently being developed. The SESR-MM is proposed as a viable instrument for assisting HEIs in the management and improvement of their SESR activities.

Control and application of minimally-actuated underwater vehicles

Establishing a persistent presence in the ocean with an Autonomous Underwater Vehicle capable of observing temporal variability of large-scale ocean processes requires a unique sensor platform. In this paper, we examine the utility of Lagrangian profiling floats for such extended deployments. We propose a strategy that utilizes ocean model predictions to facilitate a basic level of autonomy to achieve general control of this minimally-actuated underwater vehicle. We extend experimentally validated techniques for utilising ocean current models to control under-actuated autonomous underwater vehicles by presenting this investigation into the application of these methods on profiling floats. With the appropriate vertical actuation, and utilising spatiotemporal variations in water speed and direction, we show that broad controllability results can be met. First, we apply an A* planner to a local controllability map generated from predictions of ocean currents. This computes a path between start and goal waypoints that has the highest likelihood of successful execution over a given duration. The computed depth plan is generated with a model predictive controller, and selects the depths for the vehicle so that ambient currents guide it toward the goal. Mission constraints are included to simulate and motivate a practical data collection mission. Results are presented in simulation for a mission off the coast of Los Angeles, CA USA, that show surprising results in the ability of a drifting vehicle to maintain a prescribed course and reach a desired location.

Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis

Most crash severity studies ignored severity correlations between driver-vehicle units involved in the same crashes. Models without accounting for these within-crash correlations will result in biased estimates in the factor effects. This study developed a Bayesian hierarchical binomial logistic model to identify the significant factors affecting the severity level of driver injury and vehicle damage in traffic crashes at signalized intersections. Crash data in Singapore were employed to calibrate the model. Model fitness assessment and comparison using Intra-class Correlation Coefficient (ICC) and Deviance Information Criterion (DIC) ensured the suitability of introducing the crash-level random effects. Crashes occurring in peak time, in good street lighting condition, involving pedestrian injuries are associated with a lower severity, while those in night time, at T/Y type intersections, on right-most lane, and installed with red light camera have larger odds of being severe. Moreover, heavy vehicles have a better resistance on severe crash, while crashes involving two-wheel vehicles, young or aged drivers, and the involvement of offending party are more likely to result in severe injuries.

An investigation on multi-vehicle motorcycle crashes using log-linear models

Motorcyclists are the most crash-prone road-user group in many Asian countries including Singapore; however, factors influencing motorcycle crashes are still not well understood. This study examines the effects of various roadway characteristics, traffic control measures and environmental factors on motorcycle crashes at different location types including expressways and intersections. Using techniques of categorical data analysis, this study has developed a set of log-linear models to investigate multi-vehicle motorcycle crashes in Singapore. Motorcycle crash risks in different circumstances have been calculated after controlling for the exposure estimated by the induced exposure technique. Results show that night-time influence increases crash risks of motorcycles particularly during merging and diverging manoeuvres on expressways, and turning manoeuvres at intersections. Riders appear to exercise more care while riding on wet road surfaces particularly during night. Many hazardous interactions at intersections tend to be related to the failure of drivers to notice a motorcycle as well as to judge correctly the speed/distance of an oncoming motorcycle. Road side conflicts due to stopping/waiting vehicles and interactions with opposing traffic on undivided roads have been found to be as detrimental factors on motorcycle safety along arterial, main and local roads away from intersections. Based on the findings of this study, several targeted countermeasures in the form of legislations, rider training, and safety awareness programmes have been recommended.

A capability framework visualisation for multiple heterogeneous UAVs : from a mission commander's perspective

There is an increased interested in Uninhabited Aerial Vehicle (UAV) operations and research into advanced methods for commanding and controlling multiple heterogeneous UAVs. Research into areas of supervisory control has rapidly increased. Past research has investigated various approaches of autonomous control and operator limitation to improve mission commanders' Situation Awareness (SA) and cognitive workload. The aim of this paper is to address this challenge through a visualisation framework of UAV information constructed from Information Abstraction (IA). This paper presents the concept and process of IA, and the visualisation framework (constructed using IA), the concept associated with the Level Of Detail (LOD) indexing method, the visualisation of an example of the framework. Experiments will test the hypothesis that, the operator will be able to achieve increased SA and reduced cognitive load with the proposed framework.

Building Large Scale Traversability Maps Using Vehicle Experience

Traversability maps are a global spatial representation of the relative difficulty in driving through a local region. These maps support simple optimisation of robot paths and have been very popular in path planning techniques. Despite the popularity of these maps, the methods for generating global traversability maps have been limited to using a-priori information. This paper explores the construction of large scale traversability maps for a vehicle performing a repeated activity in a bounded working environment, such as a repeated delivery task.We evaluate the use of vehicle power consumption, longitudinal slip, lateral slip and vehicle orientation to classify the traversability and incorporate this into a map generated from sparse information.

Embedding human expert cognition and real-time trajectory planning in autonomous UAS

This thesis presents a new approach to compute and optimize feasible three dimensional (3D) flight trajectories using aspects of Human Decision Making (HDM) strategies, for fixed wing Unmanned Aircraft (UA) operating in low altitude environments in the presence of real time planning deadlines. The underlying trajectory generation strategy involves the application of Manoeuvre Automaton (MA) theory to create sets of candidate flight manoeuvres which implicitly incorporate platform dynamic constraints. Feasible trajectories are formed through the concatenation of predefined flight manoeuvres in an optimized manner. During typical UAS operations, multiple objectives may exist, therefore the use of multi-objective optimization can potentially allow for convergence to a solution which better reflects overall mission requirements and HDM preferences. A GUI interface was developed to allow for knowledge capture from a human expert during simulated mission scenarios. The expert decision data captured is converted into value functions and corresponding criteria weightings using UTilite Additive (UTA) theory. The inclusion of preferences elicited from HDM decision data within an Automated Decision System (ADS) allows for the generation of trajectories which more closely represent the candidate HDM’s decision strategies. A novel Computationally Adaptive Trajectory Decision optimization System (CATDS) has been developed and implemented in simulation to dynamically manage, calculate and schedule system execution parameters to ensure that the trajectory solution search can generate a feasible solution, if one exists, within a given length of time. The inclusion of the CATDS potentially increases overall mission efficiency and may allow for the implementation of the system on different UAS platforms with varying onboard computational capabilities. These approaches have been demonstrated in simulation using a fixed wing UAS operating in low altitude environments with obstacles present.

Bi-objective bundle adjustment: towards large-scale visual sea-floor mapping with a minimal sensor suite

Visual sea-floor mapping is a rapidly growing application for Autonomous Underwater Vehicles (AUVs). AUVs are well-suited to the task as they remove humans from a potentially dangerous environment, can reach depths human divers cannot, and are capable of long-term operation in adverse conditions. The output of sea-floor maps generated by AUVs has a number of applications in scientific monitoring: from classifying coral in high biological value sites to surveying sea sponges to evaluate marine environment health.

Relating vehicle generated pollutants to urban stormwater quality

Rapid urbanisation and resulting continuous increase in traffic has been recognised as key factors in the contribution of increased pollutant loads to urban stormwater and in turn to receiving waters. Urbanisation primarily increases anthropogenic activities and the percentage of impervious surfaces in urban areas. These processes are collectively responsible for urban stormwater pollution. In this regard, urban traffic and land use related activities have been recognised as the primary pollutant sources. This is primarily due to the generation of a range of key pollutants such as solids, heavy metals and PAHs. Appropriate treatment system design is the most viable approach to mitigate stormwater pollution. However, limited understanding of the pollutant process and transport pathways constrains effective treatment design. This highlights necessity for the detailed understanding of traffic and other land use related pollutants processes and pathways in relation to urban stormwater pollution. This study has created new knowledge in relation to pollutant processes and transport pathways encompassing atmospheric pollutants, atmospheric deposition and build-up on ground surfaces of traffic generated key pollutants. The research study was primarily based on in-depth experimental investigations. This thesis describes the extensive knowledge created relating to the processes of atmospheric pollutant build-up, atmospheric deposition and road surface build-up and establishing their relationships as a chain of processes. The analysis of atmospheric deposition revealed that both traffic and land use related sources contribute total suspended particulate matter (TSP) to the atmosphere. Traffic sources become dominant during weekdays whereas land use related sources become dominant during weekends due to the reduction in traffic sources. The analysis further concluded that atmospheric TSP, polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs) concentrations are highly influenced by total average daily heavy duty traffic, traffic congestion and the fraction of commercial and industrial land uses. A set of mathematical equation were developed to predict TSP, PAHs and HMs concentrations in the atmosphere based on the influential traffic and land use related parameters. Dry deposition samples were collected for different antecedent dry days and wet deposition samples were collected immediately after rainfall events. The dry deposition was found to increase with the antecedent dry days and consisted of relatively coarser particles (greater than 1.4 ìm) when compared to wet deposition. The wet deposition showed a strong affinity to rainfall depth, but was not related to the antecedent dry period. It was also found that smaller size particles (less than 1.4 ìm) travel much longer distances from the source and deposit mainly with the wet deposition. Pollutants in wet deposition are less sensitive to the source characteristics compared to dry deposition. Atmospheric deposition of HMs is not directly influenced by land use but rather by proximity to high emission sources such as highways. Therefore, it is important to consider atmospheric deposition as a key pollutant source to urban stormwater in the vicinity of these types of sources. Build-up was analysed for five different particle size fractions, namely, <1 ìm, 1-75 ìm, 75-150 ìm, 150-300 ìm and >300 ìm for solids, PAHs and HMs. The outcomes of the study indicated that PAHs and HMs in the <75 ìm size fraction are generated mainly by traffic related activities whereas the > 150 ìm size fraction is generated by both traffic and land use related sources. Atmospheric deposition is an important source for HMs build-up on roads, whereas the contribution of PAHs from atmospheric sources is limited. A comprehensive approach was developed to predict traffic and other land use related pollutants in urban stormwater based on traffic and other land use characteristics. This approach primarily included the development of a set of mathematical equations to predict traffic generated pollutants by linking traffic and land use characteristics to stormwater quality through mathematical modelling. The outcomes of this research will contribute to the design of appropriate treatment systems to safeguard urban receiving water quality for future traffic growth scenarios. The „real world. application of knowledge generated was demonstrated through mathematical modelling of solids in urban stormwater, accounting for the variability in traffic and land use characteristics.

Fusing loop detector and probe vehicle data to estimate travel time statistics on signalized urban networks

This article presents a methodology that integrates cumulative plots with probe vehicle data for estimation of travel time statistics (average, quartile) on urban networks. The integration reduces relative deviation among the cumulative plots so that the classical analytical procedure of defining the area between the plots as the total travel time can be applied. For quartile estimation, a slicing technique is proposed. The methodology is validated with real data from Lucerne, Switzerland and it is concluded that the travel time estimates from the proposed methodology are statistically equivalent to the observed values.

Physically sound vehicle-driver model for realistic microscopic simulation

In microscopic traffic simulators, the interaction between vehicles is considered. The dynamics of the system then becomes an emergent property of the interaction between its components. Such interactions include lane-changing, car-following behaviours and intersection management. Although, in some cases, such simulators produce realistic prediction, they do not allow for an important aspect of the dynamics, that is, the driver-vehicle interaction. This paper introduces a physically sound vehicle-driver model for realistic microscopic simulation. By building a nanoscopic traffic simulation model that uses steering angle and throttle position as parameters, the model aims to overcome unrealistic acceleration and deceleration values, as found in various microscopic simulation tools. A physics engine calculates the driving force of the vehicle, and the preliminary results presented here, show that, through a realistic driver-vehicle-environment simulator, it becomes possible to model realistic driver and vehicle behaviours in a traffic simulation.

Controlled emergency landing of an unpowered unmanned aerial system

The ability to perform autonomous emergency (forced) landings is one of the key technology enablers identified for UAS. This paper presents the flight test results of forced landings involving a UAS, in a controlled environment, and which was conducted to ascertain the performances of previously developed (and published) path planning and guidance algorithms. These novel 3-D nonlinear algorithms have been designed to control the vehicle in both the lateral and longitudinal planes of motion. These algorithms have hitherto been verified in simulation. A modified Boomerang 60 RC aircraft is used as the flight test platform, with associated onboard and ground support equipment sourced Off-the-Shelf or developed in-house at the Australian Research Centre for Aerospace Automation (ARCAA). HITL simulations were conducted prior to the flight tests and displayed good landing performance, however, due to certain identified interfacing errors, the flight results differed from that obtained in simulation. This paper details the lessons learnt and presents a plausible solution for the way forward.

Developing guidelines for intervention to reduce risk of low-speed vehicle run-overs of young children

In Australia, research suggests that up to one quarter of child pedestrian hospitalisations result from driveway run-over incidents (Pinkney et al., 2006). In Queensland, these numbers equate to an average of four child fatalities and 81 children presenting at hospital emergency departments every year (The Commission for Children, Young People and Child Guardian). National comparison shows that these numbers represent a slightly higher per capita rate (23.5% of all deaths). To address this issue, the current research was undertaken with the aim to develop an educative intervention based on data collected from parents and caregivers of young children. Thus, the current project did not seek to use available intervention or educational material, but to develop a new evidence-based intervention specifically targeting driveway run-overs involving young children. To this end, general behavioural and environmental changes that caregivers had undertaken in order to reduce the risk of injury to any child in their care were investigated. Broadly, the first part of this report sought to: • develop a conceptual model of established domestic safety behaviours, and to investigate whether this model could be successfully applied to the driveway setting; • explore and compare sources of knowledge regarding domestic and driveway child safety; and • examine the theoretical implications of current domestic and driveway related behaviour and knowledge among caregivers. The aim of the second part of this research was to develop and test the efficacy of an intervention based on the findings in the first part of the research project. Specifically, it sought to: • develop an educational driveway intervention that is based on current safety behaviours in the domestic setting and informed by existing knowledge of driveway safety and behaviour change theory; and • evaluate its efficacy in a sample of parents and caregivers.