Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking

Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver a vehicle to high-valued locations for data collection. We consider the use of ocean model predictions to determine the locations to be visited by an AUV, which then provides near-real time, in situ measurements back to the model to increase the skill of future predictions. The motion planning problem of steering the vehicle between the computed waypoints is not considered here. Our focus is on the algorithm to determine relevant points of interest for a chosen oceanographic feature. This represents a first approach to an end to end autonomous prediction and tasking system for aquatic, mobile sensor networks. We design a sampling plan and present experimental results with AUV retasking in the Southern California Bight (SCB) off the coast of Los Angeles.

How consistent are drivers in their driving? A driver training perspective

The value and effectiveness of driver training as a means of improving driver behaviour and road safety continues to fuel research and societal debates. Knowledge about what are the characteristics of safe driving that need to be learnt is extensive. Research has shown that young drivers are over represented in crash statistics. The encouraging fact is that novice drivers have shown improvement in road scanning pattern after training. This paper presents a driver behaviour study conducted on a closed circuit track. A group of experienced and novice drivers performed repeated multiple manoeuvres (i.e. turn, overtake and lane change) under identical conditions Variables related to the driver, vehicle and environment were recorded in a research vehicle equipped with multiple in-vehicle sensors such as GPS accelerometers, vision processing, eye tracker and laser scanner. Each group exhibited consistently a set of driving pattern characterising a particular group. Behaviour such as the indicator usage before lane change, following distance while performing a manoeuvre were among the consistent observed behaviour differentiating novice from experienced drivers. This paper will highlight the results of our study and emphasize the need for effective driver training programs focusing on young and novice drivers.

Bayesian hierarchical analysis on crash prediction models

Traditional crash prediction models, such as generalized linear regression models, are incapable of taking into account the multilevel data structure, which extensively exists in crash data. Disregarding the possible within-group correlations can lead to the production of models giving unreliable and biased estimates of unknowns. This study innovatively proposes a -level hierarchy, viz. (Geographic region level – Traffic site level – Traffic crash level – Driver-vehicle unit level – Vehicle-occupant level) Time level, to establish a general form of multilevel data structure in traffic safety analysis. To properly model the potential cross-group heterogeneity due to the multilevel data structure, a framework of Bayesian hierarchical models that explicitly specify multilevel structure and correctly yield parameter estimates is introduced and recommended. The proposed method is illustrated in an individual-severity analysis of intersection crashes using the Singapore crash records. This study proved the importance of accounting for the within-group correlations and demonstrated the flexibilities and effectiveness of the Bayesian hierarchical method in modeling multilevel structure of traffic crash data.

The experiences of parents and other supervisors in a graduated driver licensing program in Queensland, Australia

Study Aims Describe how parents and other private supervisors have responded to the changes made to the Queensland graduated driver licensing system in mid-2007 Examine differences in the experiences and perceptions of the parents and non-parents Method Combination of convenience and snowball sampling Survey administered by the internet Survey conducted between July 2009 and May 2010 Approximately 15-20 minutes to complete $20 reimbursement for participation

Mothers vs fathers as learner driver supervisors : time commitment, driving activities and perceptions of risk

The learner licence, within a graduated driver licensing system, provides new drivers with the opportunity to learn to drive under the supervision of a more experienced driver. The Queensland graduated driver licensing system requires learner drivers to record a minimum of 100 logbook hours of supervised practice with the support of parents appearing critical to ensure that this is achieved. This paper examines differences between mothers and fathers who supervise learner drivers. Mothers and fathers from Queensland who had recently supervised their child while they learnt to drive completed an internet survey about their experiences. It appears that one strategy that parents use to provide practice hours is for the child to drive themselves or their parents to or from activities that they would have attended anyway in addition to undertaking special trips in the car for the purposes of practising. The results suggest that mothers, when compared with fathers, consider driving at all stages of licensure riskier and that mothers provided more hours of supervision than fathers. However, despite this, there are limited differences between how frequently mothers and fathers provide different driving experiences such as deliberately practising in suburban areas or with passengers in the car. This research fills a gap in the literature by providing important information about the way in which parents supervise their children while they are driving on a learner licence as well as identifying some of the differences and similarities between mothers and fathers.

Differences between provisionally licensed drivers who always display P plates and those who do not

Like most Australian states, the New South Wales Graduated Driver Licensing system requires all provisionally licensed drivers to display ‘P plates’ on their vehicle to indicate their licence status and facilitate enforcement. This paper examines whether the display of P plates increases compliance with driving laws in New South Wales. The driving behaviours of provisional drivers who reported always displaying their P plates were compared with those of drivers who sometimes drove without displaying their P plates. While no differences were found between the two groups on some behaviours, provisional drivers who did not always display their P plates indicated that they were less likely to obey the provisional speed limit and more likely to break the road rules if they knew they would not be caught. These results suggest that the requirement to display a P plate remains a priority to facilitate more general traffic law enforcement initiatives.

Driver licensing experience of Korean Australians

Graduated licensing has been identified as the most promising approach to reducing the crash risk of novice drivers. However, research suggests that the effectiveness of graduated licensing appears to differ between urban and rural novice drivers and according to race or ethnicity. Extensive supervised driving practice as a learner driver is an important component of graduated licensing systems in Australia and many other countries. Earlier CARRS-Q research identified that falsification of logbooks was more common among particular demographic groups. The factors underlying this are not well understood. It is unclear whether this reflects a lack of understanding of the importance of supervised practice (given that it is not a licensing requirement in many countries of origin), or it reflects lack of access to vehicles and supervising drivers, or whether there is less respect for driver licensing requirements among some groups. It is possible that the importance of these factors may differ across ethnic groups, depending on socioeconomic factors and cultural attitudes to road safety. In an attempt to better understand these issues, this study presents some preliminary results of focus groups examining the experience of the Queensland Graduated Driver Licensing System by Korean-Australian novice drivers and their parents.

Shared autonomy for close-quarters navigation and control of a VTOL platform

This thesis presents an approach for a vertical infrastructure inspection using a vertical take-off and landing (VTOL) unmanned aerial vehicle and shared autonomy. Inspecting vertical structure such as light and power distribution poles is a difficult task. There are challenges involved with developing such an inspection system, such as flying in close proximity to a target while maintaining a fixed stand-off distance from it. The contributions of this thesis fall into three main areas. Firstly, an approach to vehicle dynamic modeling is evaluated in simulation and experiments. Secondly, EKF-based state estimators are demonstrated, as well as estimator-free approaches such as image based visual servoing (IBVS) validated with motion capture ground truth data. Thirdly, an integrated pole inspection system comprising a VTOL platform with human-in-the-loop control, (shared autonomy) is demonstrated. These contributions are comprehensively explained through a series of published papers.

Fuzzy logic to evaluate driving maneuvers: An integrated approach to improve training

Driver training is one of the interventions aimed at mitigating the number of crashes that involve novice drivers. Our failure to understand what is really important for learners, in terms of risky driving, is one of the many drawbacks restraining us to build better training programs. Currently, there is a need to develop and evaluate Advanced Driving Assistance Systems that could comprehensively assess driving competencies. The aim of this paper is to present a novel Intelligent Driver Training System (IDTS) that analyses crash risks for a given driving situation, providing avenues for improvement and personalisation of driver training programs. The analysis takes into account numerous variables acquired synchronously from the Driver, the Vehicle and the Environment (DVE). The system then segments out the manoeuvres within a drive. This paper further presents the usage of fuzzy set theory to develop the safety inference rules for each manoeuvre executed during the drive. This paper presents a framework and its associated prototype that can be used to comprehensively view and assess complex driving manoeuvres and then provide a comprehensive analysis of the drive used to give feedback to novice drivers.

A neurofuzzy approach to future intelligent driver support systems

This paper reviews a variety of advanced signal processing algorithms that have been developed at the University of Southampton as part of the Prometheus (PROgraMme for European Traffic flow with Highest Efficiency and Unprecedented Safety) research programme to achieve an intelligent driver warning system (IDWS). The IDWS includes: visual detection of both generic obstacles and other vehicles, together with their tracking and identification, estimates of time to collision and behavioural modelling of drivers for a variety of scenarios. These application areas are used to show the applicability of neurofuzzy techniques to the wide range of problems required to support an IDWS, and for future fully autonomous vehicles.

Modeling stochastic hybrid systems

Stochastic hybrid systems arise in numerous applications of systems with multiple models; e.g., air traffc management, flexible manufacturing systems, fault tolerant control systems etc. In a typical hybrid system, the state space is hybrid in the sense that some components take values in a Euclidean space, while some other components are discrete. In this paper we propose two stochastic hybrid models, both of which permit diffusion and hybrid jump. Such models are essential for studying air traffic management in a stochastic framework.