652,807 research outputs found

    Work domain analysis and intelligent transport systems: Implications for vehicle design

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    This article presents a Work Domain Analysis (WDA) of the road transport system in Victoria, Australia. A series of driver information requirements and tasks that could potentially be supported through the use of Intelligent Transport Systems (ITS) are then extracted from the WDA. The potential use of ITS technologies to circumvent these information gaps and provide additional support to drivers is discussed. It is concluded that driver information requirements are currently not entirely satisfied by contemporary vehicle design and also that there are a number of driving tasks that could be further supported through the provision of supplementary systems within vehicles

    Smart driving aids and their effects on driving performance and driver distraction

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    In-vehicle information systems have been shown to increase driver workload and cause distraction; both of which are causal factors for accidents. This simulator study evaluates the impact that two designs for a smart driving aid, and scenario complexity have on workload, distraction and driving performance. Results showed that real-time delivery of smart driving information did not increase driver workload or adversely effect driver distraction, while having the effect of decreasing mean driving speed in both the simple and complex driving scenarios. Subjective workload was shown to increase with task difficulty, as well as revealing important differences between the two interface designs

    Smart driving assistance systems : designing and evaluating ecological and conventional displays

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    In-vehicle information systems have been shown to increase driver workload and cause distraction; both are causal factors for accidents. This simulator study evaluates the impact that two designs for a smart driving aid and scenario complexity has on workload, distraction and driving performance. Results showed that real-time delivery of smart driving information did not increase driver workload or adversely affect driver distraction, while having the effect of decreasing mean driving speed in both the simple and complex driving scenarios. Important differences were also highlighted between conventional and ecologically designed smart driving interfaces with respect to subjective workload and peripheral detection

    Assessing Crash Risks on Curves

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    In Queensland, curve related crashes contributed to 63.44% of fatalities, and 25.17% required hospitalisation. In addition, 51.1% of run-off-road crashes occurred on obscured or open-view road curves (Queensland Transport, 2006). This paper presents a conceptual framework for an in-vehicle system, which assesses crash risk when a driver is manoeuvring on a curve. Our approach consists of using Intelligent Transport Systems (ITS) to collect information about the driving context. The driving context corresponds to information about the environment, driver, and vehicle gathered from sensor technology. Sensors are useful to detect drivers’ high-risk situations such as curves, fogs, drivers’ fatigue or slippery roads. However, sensors can be unreliable, and therefore the information gathered from them can be incomplete or inaccurate. In order to improve the accuracy, a system is built to perform information fusion from past and current driving information. The integrated information is analysed using ubiquitous data mining techniques and the results are later used in a Coupled Hidden Markov Model (CHMM), to learn and classify the information into different risk categories. CHMM is used to predict the probability of crash on curves. Based on the risk assessment, our system provides appropriate intervention to the driver. This approach could allow the driver to have sufficient time to react promptly. Hence, this could potentially promote safe driving and decrease curve related injuries and fatalities

    microSlotted 1-Persistence Flooding in VANETs

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    Many Driver Support Systems in future vehicles will rely on wireless communication. This wireless communication can be divided into two categories: Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2V is often used for vehicles to exchange information of a local nature, e.g. co-operative following or collision avoidance. V2I can be used as ’smart road signs’, access to back-end networks (e.g. Internet) or as simple repeaters. The term VANET is key to V2V and V2I communication: Vehicular Ad hoc Network. A Driver Support System described in [1] presents an interesting problem: a vehicle should be aware of the state of traffic on a road, up to several kilometers ahead. A system called the TraffiFilter has been proposed in [2] to provide this information

    Driver Information Systems for Highway-Railway Grade Crossings

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    Using Travel Simulation to Investigate Driver Response to In-Vehicle Route Guidance Systems,

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    A major application for developed satellite navigation systems is the in-vehicle route guidance market. As systems become cheaper to purchase and easier to install and indeed car manufacturers begin to fit the equipment as standard in new vehicles, the potential market for such systems in the developed world is massive. But what are the consequences of giving navigational assistance to car drivers? How will drivers respond to this information? Such information is liable to have a big impact upon driver route choice behaviour and is also subject to their interpretation of the guidance and action upon receiving it. This response may change under different travel circumstances. The impact of collective response to driver guidance is also of importance to traffic engineers and city planners, since routing through environmentally sensitive areas or heavily congested corridors should be avoided. The overall network effects are therefore of key importance to ensure efficient routing and minimal disruption to the road network. It is quite difficult to observe real-life behaviour on a consistent basis, since there are so many confounding variables in the real-world, traffic is never the same two days running, let alone hour by hour and a rigorous experimental environment is required, since control of experimental conditions is paramount to being able to confidently predict driver behaviour in response to navigational aids. Also the take up of guidance systems is still in its infancy, so far available only to a niche market of specialist professionals and those with disposable income. A need to test the common publics’ response to route guidance systems is therefore required. The development of travel simulation techniques, using portable computers and specialist software, gives robust experimental advantages. Although not totally realistic of the driving task, these techniques are sufficient in their realism of the decision element of route selection, enough to conduct experimental studies into drivers’ route choice behaviour under conditions of receiving simulated guidance advice. In this manner driver response to in-vehicle route guidance systems can be tested under a range of hypothetical journey making travel scenarios. This paper will outline the development of travel simulation techniques as a tool for in-vehicle route guidance research, including different methods and key simulation design requirements. The second half of the paper will report in detail on the findings from a recently conducted experiment investigating drivers’ response to route guidance when in familiar and unfamiliar road networks. The results will indicate the importance of providing meaningful information to drivers under these two real-life circumstances and report on how demands for route guidance information may vary by type of journey. Findings indicate that the guidance acceptance need not only depend on the optimum route choice criteria, it is also affected by network familiarity, quality and credibility of guidance advice and personal attributes of the drivers
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