Novel car carrier design : prevention of falls from heights

This article reports the details of a research on novel design in the field of semitrailer sector and discuss design by hazard prevention techniques. The novel design made addresses occupational health and safety (OHS)concerns of fall from heights. The research includes a detailed survey of national data sources to examine the fatalities caused due to fall from heights in car carriers. The study investigates OHS recommendations in Australia for semitrailer sector. Often injuries are caused due to drivers working above the 1.5 meter height for loading, unloading of the cars, moving the decks up, down, strapping the cars, and slipperly. The new design is developed using latest computer aided design and engineeing (CAD, CAE), product data management (PDM), virtual design process (VDP). The new car carrier design excels in reducing the risks of injuries to drivers and new bench mark for OHS standards. The new design has all the decks operated with hydraulics and uses unique ratchet lock mechanism (fool proof design) and loading happens at a safe working height (below 1.5 meter). All the cars are strapped on the safe working height, and then car desks operated hydraulically to transfer them to the required position. This also includes the car on the prime mover, which shuttles across from one deck to other using hydraulic and rack-pinion mechanisms. The novel design car carrier solves the problem of falls from height: next step would be to transfer this technology across other similar effected sectors.

V2V/V2I augmented maps : state-of-the-art and contribution to real-time crash risk assessment

A number of advanced driver assistance systems (ADAS) are currently being released on the market, providing safety functions to the drivers such as collision avoidance, adaptive cruise control or enhanced night-vision. These systems however are inherently limited by their sensory range: they cannot gather information from outside this range, also called their “perceptive horizon”. Cooperative systems are a developing research avenue that aims at providing extended safety and comfort functionalities by introducing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) wireless communications to the road actors. This paper presents the problematic of cooperative systems, their advantages and contributions to road safety and exposes some limitations related to market penetration, sensors accuracy and communications scalability. It explains the issues of how to implement extended perception, a central contribution of cooperative systems. The initial steps of an evaluation of data fusion architectures for extended perception are exposed.

A grouping model for distributed pipeline assets maintenance decision

Distributed pipeline assets systems are crucial to society. The deterioration of these assets and the optimal allocation of limited budget for their maintenance correspond to crucial challenges for water utility managers. Decision makers should be assisted with optimal solutions to select the best maintenance plan concerning available resources and management strategies. Much research effort has been dedicated to the development of optimal strategies for maintenance of water pipes. Most of the maintenance strategies are intended for scheduling individual water pipe. Consideration of optimal group scheduling replacement jobs for groups of pipes or other linear assets has so far not received much attention in literature. It is a common practice that replacement planners select two or three pipes manually with ambiguous criteria to group into one replacement job. This is obviously not the best solution for job grouping and may not be cost effective, especially when total cost can be up to multiple million dollars. In this paper, an optimal group scheduling scheme with three decision criteria for distributed pipeline assets maintenance decision is proposed. A Maintenance Grouping Optimization (MGO) model with multiple criteria is developed. An immediate challenge of such modeling is to deal with scalability of vast combinatorial solution space. To address this issue, a modified genetic algorithm is developed together with a Judgment Matrix. This Judgment Matrix is corresponding to various combinations of pipe replacement schedules. An industrial case study based on a section of a real water distribution network was conducted to test the new model. The results of the case study show that new schedule generated a significant cost reduction compared with the schedule without grouping pipes.

Structural reliability prediction of a steel bridge element using Dynamic Object Oriented Bayesian Network (DOOBN)

Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method.

Real-time evaluation of driver’s alertness on highways

Suburbanisation has been internationally a major phenomenon in the last decades. Suburb-to-suburb routes are nowadays the most widespread road journeys; and this resulted in an increment of distances travelled, particularly on faster suburban highways. The design of highways tends to over-simplify the driving task and this can result in decreased alertness. Driving behaviour is consequently impaired and drivers are then more likely to be involved in road crashes. This is particularly dangerous on highways where the speed limit is high. While effective countermeasures to this decrement in alertness do not currently exist, the development of in-vehicle sensors opens avenues for monitoring driving behaviour in real-time. The aim of this study is to evaluate in real-time the level of alertness of the driver through surrogate measures that can be collected from in-vehicle sensors. Slow EEG activity is used as a reference to evaluate driver's alertness. Data are collected in a driving simulator instrumented with an eye tracking system, a heart rate monitor and an electrodermal activity device (N=25 participants). Four different types of highways (driving scenario of 40 minutes each) are implemented through the variation of the road design (amount of curves and hills) and the roadside environment (amount of buildings and traffic). We show with Neural Networks that reduced alertness can be detected in real-time with an accuracy of 92% using lane positioning, steering wheel movement, head rotation, blink frequency, heart rate variability and skin conductance level. Such results show that it is possible to assess driver's alertness with surrogate measures. Such methodology could be used to warn drivers of their alertness level through the development of an in-vehicle device monitoring in real-time drivers' behaviour on highways, and therefore it could result in improved road safety.

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.

Collision warning dissemination in vehicles strings : an empirical measurement

Inter-Vehicular Communications (IVC) are considered a promising technological approach for enhancing transportation safety and improving highway efficiency. Previous theoretical work has demonstrated the benefits of IVC in vehicles strings. Simulations of partially IVC-equipped vehicles strings showed that only a small equipment ratio is sufficient to drastically reduce the number of head on collisions. However, these results are based on the assumptions that IVC exhibit lossless and instantaneous messages transmission. This paper presents the research design of an empirical measurement of a vehicles string, with the goal of highlighting the constraints introduced by the actual characteristics of communication devices. A warning message diffusion system based on IEEE 802.11 wireless technology was developed for an emergency breaking scenario. Preliminary results are presented as well, showing the latencies introduced by using 802.11a and discussing early findings and experimental limitations

Condition deterioration prediction of bridge elements using Dynamic Bayesian Networks (DBNs)

The ability of bridge deterioration models to predict future condition provides significant advantages in improving the effectiveness of maintenance decisions. This paper proposes a novel model using Dynamic Bayesian Networks (DBNs) for predicting the condition of bridge elements. The proposed model improves prediction results by being able to handle, deterioration dependencies among different bridge elements, the lack of full inspection histories, and joint considerations of both maintenance actions and environmental effects. With Bayesian updating capability, different types of data and information can be utilised as inputs. Expert knowledge can be used to deal with insufficient data as a starting point. The proposed model established a flexible basis for bridge systems deterioration modelling so that other models and Bayesian approaches can be further developed in one platform. A steel bridge main girder was chosen to validate the proposed model.

Empirical IEEE 802.11p performance evaluation on test tracks

IEEE 802.11p is the new standard for inter-vehicular communications (IVC) using the 5.9 GHz frequency band; it is planned to be widely deployed to enable cooperative systems. 802.11p uses and performance have been studied theoretically and in simulations over the past years. Unfortunately, many of these results have not been confirmed by on-tracks experimentation. In this paper, we describe field trials of 802.11p technology with our test vehicles. Metrics such as maximum range, latency and frame loss are examined.

Simulation architecture for the design of cooperative collision warning systems

Simulation has been widely used to estimate the benefits of Cooperative Systems (CS) based on Inter-Vehicular Communications (IVC). This paper presents a new architecture built with the SiVIC simulator and the RTMaps™ multisensors prototyping platform. We introduce several improvements from a previous similar architecture, regarding IVC modelisation and vehicles’ control. It has been tuned with on-road measurements to improve fidelity. We discuss the results of a freeway emergency braking scenario (EEBL) implemented to validate our architecture’s capabilities.

Simulation architecture for cooperative ITS applications and augmented perception

Cooperative Systems provide, through the multiplication of information sources over the road, a lot of potential to improve the safety of road users, especially drivers. However, developing cooperative ITS applications requires additional resources compared to non-cooperative applications which are both time consuming and expensive. In this paper, we present a simulation architecture aimed at prototyping cooperative ITS applications in an accurate and detailed, close-to-reality environment; the architecture is designed to be modular and generalist. It can be used to simulate any type of CS applications as well as augmented perception. Then, we discuss the results of two applications deployed with our architecture, using a common freeway emergency braking scenario. The first application is Emergency Electronic Brake Light (EEBL); we discuss improvements in safety in terms of the number of crashes and the severity of crashes. The second application compares the performance of a cooperative risk assessment using an augmented map against a non-cooperative approach based on local-perception only. Our results show a systematic improvement of forward warning time for most vehicles in the string when using the augmented-map-based risk assessment.

Comparing cooperative and non-cooperative crash risk assessment

Cooperative Systems provide, through the multiplication of information sources over the road, a lot of potential to improve the assessment of the road risk describing a particular driving situation. In this paper, we compare the performance of a cooperative risk assessment approach against a non-cooperative approach; we used an advanced simulation framework, allowing for accurate and detailed, close-to-reality simulations. Risk is estimated, in both cases, with combinations of indicators based on the TTC. For the non-cooperative approach, vehicles are equipped only with an AAC-like forward-facing ranging sensor. On the other hand, for the cooperative approach, vehicles share information through 802.11p IVC and create an augmented map representing their environment; risk indicators are then extracted from this map. Our system shows that the cooperative risk assessment provides a systematic increase of forward warning to most of the vehicles involved in a freeway emergency braking scenario, compared to a non-cooperative system.