Association between glaucoma and at–fault motor vehicle collision involvement among older drivers

Objective To examine the association between glaucoma and motor vehicle collision (MVC) involvement among older drivers, including the role of visual field impairment that may underlie any association found. Design A retrospective population-based study Participants A sample of 2,000 licensed drivers aged 70 years and older who reside in north central Alabama. Methods At-fault MVC involvement for five years prior to enrollment was obtained from state records. Three aspects of visual function were measured: habitual binocular distance visual acuity, binocular contrast sensitivity and the binocular driving visual field constructed from combining the monocular visual fields of each eye. Poisson regression was used to calculate crude and adjusted rate ratios (RR) and 95% confidence intervals (CI). Main Outcomes Measures At-fault MVC involvement for five years prior to enrollment. Results Drivers with glaucoma (n = 206) had a 1.65 (95% confidence interval [CI] 1.20-2.28, p = 0.002) times higher MVC rate compared to those without glaucoma after adjusting for age, gender and mental status. Among those with glaucoma, drivers with severe visual field loss had higher MVC rates (RR = 2.11, 95% CI 1.09-4.09, p = 0.027), whereas no significant association was found among those with impaired visual acuity and contrast sensitivity. When the visual field was sub-divided into six regions (upper, lower, left, and right visual fields; horizontal and vertical meridians), we found that impairment in the left, upper or lower visual field was associated with higher MVC rates, and an impaired left visual field showed the highest RR (RR = 3.16, p = 0.001) compared to other regions. However, no significant association was found in deficits in the right side or along the horizontal or vertical meridian. Conclusions A population-based study suggests that older drivers with glaucoma are more likely to have a history of at-fault MVC involvement than those without glaucoma. Impairment in the driving visual field in drivers with glaucoma appears to have an independent association with at-fault MVC involvement, whereas visual acuity and contrast sensitivity impairments do not.

Safety culture and speeding in the Australian heavy vehicle industry

Inappropriate speed and speeding are among the highest causes of crashes in the heavy vehicle industry. Truck drivers are subjected to a broad range of influences on their behaviour including industrial pressures, company monitoring and police enforcement. Further, drivers have a high level of autonomy over their own behaviour. As such it is important to understand how these external influences interact with commonly shared beliefs, attitudes and values of heavy vehicle drivers to influence their behaviour. The present study uses a re-conceptualisation of safety culture to explore the behaviours of driving at an inappropriate speed and speeding in the heavy vehicle industry. A series of case studies, consisting of interviews and ride-along observations, were conducted with three transport organisations to explore the effect of culture on safety in the heavy vehicle industry. Results relevant to inappropriate speed are reported and discussed. It was found that organisational management through monitoring, enforcement and payment, police enforcement, customer standards and vehicle design factors could all reduce the likelihood of driving at inappropriate speeds under some circumstances. However, due to weaknesses in the ability to accurately monitor appropriate speed, this behaviour was primarily influenced by cultural beliefs, attitudes and values. Truck drivers had a tendency to view speeding as relatively safe, had a desire to speed to save time and increase personal income, and thus often attempted to speed without detection. When drivers saw speeding as dangerous, however, they were more likely to drive safely. Implications for intervention are discussed.

Characteristics of road factors in multi and single vehicle motorcycle crashes in Queensland

Motorcyclists were involved in 6.4% of all police-reported crashes and 12.5% of all fatal crashes in Queensland during 2004-2011. Of these crashes, 43% were single-vehicle (SV) and 57% were multi-vehicle (MV). The overall reduction in motorcycle crashes in this period masked different trends: single-vehicle crashes increased while MV motorcycle crashes decreased. However, little research has been undertaken to understand the similarities and differences between SV and MV motorcycle crashes in Queensland and the factors underlying these diverging trends. The descriptive analyses and regression model developed here confirm international research findings regarding the greater role of road infrastructure factors in SV crashes. In particular, road geometric factors such as horizontal and vertical alignment and road surface factors such as sealed/unsealed and wet/dry were more important in SV than MV crashes.

Energy-based guidance of an underactuated unmanned underwater vehicle on a helical trajectory

This paper presents a motion control system for guidance of an underactuated Unmanned Underwater Vehicle (UUV) on a helical trajectory. The control strategy is developed using Port-Hamiltonian theory and interconnection and damping assignment passivity-based control. Using energy routing, the trajectory of a virtual fully actuated plant is guided onto a vector field. A tracking controller is then used that commands the underactuated plant to follow the velocity of the virtual plant. An integral control is inserted between the two control layers, which adds robustness and disturbance rejection to the design.

Energy-based motion control of a slender hull unmanned underwater vehicle

This paper presents a motion control system for tracking of attitude and speed of an underactuated slender-hull unmanned underwater vehicle. The feedback control strategy is developed using the Port-Hamiltonian theory. By shaping of the target dynamics (desired dynamic response in closed loop) with particular attention to the target mass matrix, the influence of the unactuated dynamics on the controlled system is suppressed. This results in achievable dynamics independent of stable uncontrolled states. Throughout the design, the insight of the physical phenomena involved is used to propose the desired target dynamics. Integral action is added to the system for robustness and to reject steady disturbances. This is achieved via a change of coordinates that result in input-to-state stable (ISS) target dynamics. As a final step in the design, an anti-windup scheme is implemented to account for limited actuator capacity, namely saturation. The performance of the design is demonstrated through simulation with a high-fidelity model.

Maximal regularity for second order non-autonomous Cauchy problems

We consider some non-autonomous second order Cauchy problems of the form u + B(t)(u) over dot + A(t)u = f (t is an element of [0, T]), u(0) = (u) over dot(0) = 0. We assume that the first order problem (u) over dot + B(t)u = f (t is an element of [0, T]), u(0) = 0, has L-p-maximal regularity. Then we establish L-p-maximal regularity of the second order problem in situations when the domains of B(t(1)) and A(t(2)) always coincide, or when A(t) = kappa B(t).

From vision to actions: Towards adaptive and autonomous humanoid robots

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions.

Electrical Characterization of Airborne Vehicle Exhaust Plume

he induced current and voltage on the skin of an airborne vehicle due to the coupling of external electromagnetic field could be altered in the presence of ionized exhaust plume. So in the present work, a theoretical analysis is done to estimate the electrical parameters such as electrical conductivity and permittivity and their distribution in the axial and radial directions of the exhaust plume of an airborne vehicle. The electrical conductivity depends on the distribution of the major ionic species produced from the propellant combustion. In addition it also depends on temperature and pressure distribution of the exhaust plume as well as the generated shock wave. The chemically reactive rocket exhaust flow is modeled in two stages. The first part is simulated from the combustion chamber to the throat of the supersonic nozzle by using NASA Chemical Equilibrium with Application (CEA) package and the second part is simulated from the nozzle throat to the downstream of the plume by using a commercial Computational Fluid Dynamics (CFD) solver. The contour plots of the exhaust parameters are presented. Eight barrel shocks which influence the distribution of the vehicle exhaust parameters are obtained in this simulation. The computed peak value of the electrical conductivity of the plume is 0.123 S/m and the relative permittivity varies from 0.89 to 0.99. The attenuation of the microwave when it is passing through the conducting exhaust plume has also been presented.

Validation of a 4DOF manoeuvring model of a high-speed vehicle-passenger trimaran

This paper presents the validation of a manoeuvring model for a novel 127m-vehicle-passenger trimaran via full scale trials. The adopted structure of the model is based on a model previously proposed in the literature with some simplifications. The structure of the model is discussed. Then initial parameter estimates are computed, and the final set of parameters are obtained via adjustments based on engineering judgement and application of a genetic algorithm so as to match the data of the trials. The validity of the model is also assessed with data from a trial different from the one use for the parameter adjustment. The model shows good agreement with the trial data.

Proposed vehicle impact speed: Severe injury probability relationships for selected crash types

Speed is recognised as a key contributor to crash likelihood and severity, and to road safety performance in general. Its fundamental role has been recognised by making Safe Speeds one of the four pillars of the Safe System. In this context, impact speeds above which humans are likely to sustain fatal injuries have been accepted as a reference in many Safe System infrastructure policy and planning discussions. To date, there have been no proposed relationships for impact speeds above which humans are likely to sustain fatal or serious (severe) injury, a more relevant Safe System measure. A research project on Safe System intersection design required a critical review of published literature on the relationship between impact speed and probability of injury. This has led to a number of questions being raised about the origins, accuracy and appropriateness of the currently accepted impact speed–fatality probability relationships (Wramborg 2005) in many policy documents. The literature review identified alternative, more recent and more precise relationships derived from the US crash reconstruction databases (NASS/CDS). The paper proposes for discussion a set of alternative relationships between vehicle impact speed and probability of MAIS3+ (fatal and serious) injury for selected common crash types. Proposed Safe System critical impact speed values are also proposed for use in road infrastructure assessment. The paper presents the methodology and assumptions used in developing these relationships. It identifies further research needed to confirm and refine these relationships. Such relationships would form valuable inputs into future road safety policies in Australia and New Zealand.

A Survey of autonomous vision-based See and Avoid for Unmanned Aircraft Systems

This paper provides a comprehensive review of the vision-based See and Avoid problem for unmanned aircraft. The unique problem environment and associated constraints are detailed, followed by an in-depth analysis of visual sensing limitations. In light of such detection and estimation constraints, relevant human, aircraft and robot collision avoidance concepts are then compared from a decision and control perspective. Remarks on system evaluation and certification are also included to provide a holistic review approach. The intention of this work is to clarify common misconceptions, realistically bound feasible design expectations and offer new research directions. It is hoped that this paper will help us to unify design efforts across the aerospace and robotics communities.

A novel approach to 6-DOF adaptive trajectory tracking control of an AUV in the presence of parameter uncertainties

In this paper, the trajectory tracking control of an autonomous underwater vehicle (AUVs) in six-degrees-of-freedom (6-DOFs) is addressed. It is assumed that the system parameters are unknown and the vehicle is underactuated. An adaptive controller is proposed, based on Lyapunov׳s direct method and the back-stepping technique, which interestingly guarantees robustness against parameter uncertainties. The desired trajectory can be any sufficiently smooth bounded curve parameterized by time even if consist of straight line. In contrast with the majority of research in this field, the likelihood of actuators׳ saturation is considered and another adaptive controller is designed to overcome this problem, in which control signals are bounded using saturation functions. The nonlinear adaptive control scheme yields asymptotic convergence of the vehicle to the reference trajectory, in the presence of parametric uncertainties. The stability of the presented control laws is proved in the sense of Lyapunov theory and Barbalat׳s lemma. Efficiency of presented controller using saturation functions is verified through comparing numerical simulations of both controllers.

Design and Development of an In-Vehicle Human Machine Interface to Improve Fuel Efficiency & Safety

Road transport plays a significant role in various industries and mobility services around the globe and has a vital impact on our daily lives. However it also has serious impacts on both public health and the environment. In-vehicle feedback systems are a relatively new approach to encouraging driver behaviour change for improving fuel efficiency and safety in automotive environments. While many studies claim that the adoption of eco-driving practices, such as eco-driving training programs and in-vehicle feedback to drivers, has the potential to improve fuel efficiency, limited research has integrated safety and eco-driving. Therefore, this research seeks to use human factors related theories and practices to inform the design and evaluation of an in-vehicle Human Machine Interface (HMI) providing real-time driver feedback with the aim of improving both fuel efficiency and safety.

Autonomous UAVs wildlife detection using thermal imaging, predictive navigation and computer vision

There is an increased interest on the use of Unmanned Aerial Vehicles (UAVs) for wildlife and feral animal monitoring around the world. This paper describes a novel system which uses a predictive dynamic application that places the UAV ahead of a user, with a low cost thermal camera, a small onboard computer that identifies heat signatures of a target animal from a predetermined altitude and transmits that target’s GPS coordinates. A map is generated and various data sets and graphs are displayed using a GUI designed for easy use. The paper describes the hardware and software architecture and the probabilistic model for downward facing camera for the detection of an animal. Behavioral dynamics of target movement for the design of a Kalman filter and Markov model based prediction algorithm are used to place the UAV ahead of the user. Geometrical concepts and Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of the user, thus delivering a new way point for autonomous navigation. Results show that the system is capable of autonomously locating animals from a predetermined height and generate a map showing the location of the animals ahead of the user.

A Bayesian latent class safety performance function for identifying motor vehicle crash blackspots

The current state of the practice in Blackspot Identification (BSI) utilizes safety performance functions based on total crash counts to identify transport system sites with potentially high crash risk. This paper postulates that total crash count variation over a transport network is a result of multiple distinct crash generating processes including geometric characteristics of the road, spatial features of the surrounding environment, and driver behaviour factors. However, these multiple sources are ignored in current modelling methodologies in both trying to explain or predict crash frequencies across sites. Instead, current practice employs models that imply that a single underlying crash generating process exists. The model mis-specification may lead to correlating crashes with the incorrect sources of contributing factors (e.g. concluding a crash is predominately caused by a geometric feature when it is a behavioural issue), which may ultimately lead to inefficient use of public funds and misidentification of true blackspots. This study aims to propose a latent class model consistent with a multiple crash process theory, and to investigate the influence this model has on correctly identifying crash blackspots. We first present the theoretical and corresponding methodological approach in which a Bayesian Latent Class (BLC) model is estimated assuming that crashes arise from two distinct risk generating processes including engineering and unobserved spatial factors. The Bayesian model is used to incorporate prior information about the contribution of each underlying process to the total crash count. The methodology is applied to the state-controlled roads in Queensland, Australia and the results are compared to an Empirical Bayesian Negative Binomial (EB-NB) model. A comparison of goodness of fit measures illustrates significantly improved performance of the proposed model compared to the NB model. The detection of blackspots was also improved when compared to the EB-NB model. In addition, modelling crashes as the result of two fundamentally separate underlying processes reveals more detailed information about unobserved crash causes.