6,050 research outputs found

    A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

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    open access articleDetecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi- autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, drivers’ activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi- class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driver’s distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context

    Resilience Assignment Framework using System Dynamics and Fuzzy Logic.

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    This paper is concerned with the development of a conceptual framework that measures the resilience of the transport network under climate change related events. However, the conceptual framework could be adapted and quantified to suit each disruption’s unique impacts. The proposed resilience framework evaluates the changes in transport network performance in multi-stage processes; pre, during and after the disruption. The framework will be of use to decision makers in understanding the dynamic nature of resilience under various events. Furthermore, it could be used as an evaluation tool to gauge transport network performance and highlight weaknesses in the network. In this paper, the system dynamics approach and fuzzy logic theory are integrated and employed to study three characteristics of network resilience. The proposed methodology has been selected to overcome two dominant problems in transport modelling, namely complexity and uncertainty. The system dynamics approach is intended to overcome the double counting effect of extreme events on various resilience characteristics because of its ability to model the feedback process and time delay. On the other hand, fuzzy logic is used to model the relationships among different variables that are difficult to express in numerical form such as redundancy and mobility

    Risk analysis of autonomous vehicle and its safety impact on mixed traffic stream

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    In 2016, more than 35,000 people died in traffic crashes, and human error was the reason for 94% of these deaths. Researchers and automobile companies are testing autonomous vehicles in mixed traffic streams to eliminate human error by removing the human driver behind the steering wheel. However, recent autonomous vehicle crashes while testing indicate the necessity for a more thorough risk analysis. The objectives of this study were (1) to perform a risk analysis of autonomous vehicles and (2) to evaluate the safety impact of these vehicles in a mixed traffic stream. The overall research was divided into two phases: (1) risk analysis and (2) simulation of autonomous vehicles. Risk analysis of autonomous vehicles was conducted using the fault tree method. Based on failure probabilities of system components, two fault tree models were developed and combined to predict overall system reliability. It was found that an autonomous vehicle system could fail 158 times per one-million miles of travel due to either malfunction in vehicular components or disruption from infrastructure components. The second phase of this research was the simulation of an autonomous vehicle, where change in crash frequency after autonomous vehicle deployment in a mixed traffic stream was assessed. It was found that average travel time could be reduced by about 50%, and 74% of conflicts, i.e., traffic crashes, could be avoided by replacing 90% of the human drivers with autonomous vehicles

    Modelling of Driver and Pedestrian Behaviour – A Historical Review

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    Driver and pedestrian behaviour significantly affect the safety and the flow of traffic at the microscopic and macroscopic levels. The driver behaviour models describe the driver decisions made in different traffic flow conditions. Modelling the pedestrian behaviour plays an essential role in the analysis of pedestrian flows in the areas such as public transit terminals, pedestrian zones, evacuations, etc. Driver behaviour models, integrated into simulation tools, can be divided into car-following models and lane-changing models. The simulation tools are used to replicate traffic flows and infer certain regularities. Particular model parameters must be appropriately calibrated to approximate the realistic traffic flow conditions. This paper describes the existing car-following models, lane-changing models, and pedestrian behaviour models. Further, it underlines the importance of calibrating the parameters of microsimulation models to replicate realistic traffic flow conditions and sets the guidelines for future research related to the development of new models and the improvement of the existing ones.</p

    Context-aware GPS Integrity Monitoring for Intelligent Transport Systems (ITS)

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    The integrity of positioning systems has become an increasingly important requirement for location-based Intelligent Transports Systems (ITS). The navigation systems, such as Global Positioning System (GPS), used in ITS cannot provide the high quality positioning information required by most services, due to the various type of errors from GPS sensor, such as signal outage, and atmospheric effects, all of which are difficult to measure, or from the map matching process. Consequently, an error in the positioning information or map matching process may lead to inaccurate determination of a vehicle’s location. Thus, the integrity is require when measuring both vehicle’s positioning and other related information such as speed, to locate the vehicle in the correct road segment, and avoid errors. The integrity algorithm for the navigation system should include a guarantee that the systems do not produce misleading or faulty information; as this may lead to a significant error arising in the ITS services. Hence, to achieve the integrity requirement a navigation system should have a robust mechanism, to notify the user of any potential errors in the navigation information. The main aim of this research is to develop a robust and reliable mechanism to support the positioning requirement of ITS services. This can be achieved by developing a high integrity GPS monitoring algorithm with the consideration of speed, based on the concept of context-awareness which can be applied with real time ITS services to adapt changes in the integrity status of the navigation system. Context-aware architecture is designed to collect contextual information about the vehicle, including location, speed and heading, reasoning about its integrity and reactions based on the information acquired. In this research, three phases of integrity checks are developed. These are, (i) positioning integrity, (ii) speed integrity, and (iii) map matching integrity. Each phase uses different techniques to examine the consistency of the GPS information. A receiver autonomous integrity monitoring (RAIM) algorithm is used to measure the quality of the GPS positioning data. GPS Doppler information is used to check the integrity of vehicle’s speed, adding a new layer of integrity and improving the performance of the map matching process. The final phase in the integrity algorithm is intended to verify the integrity of the map matching process. In this phase, fuzzy logic is also used to measure the integrity level, which guarantees the validity and integrity of the map matching results. This algorithm is implemented successfully, examined using real field data. In addition, a true reference vehicle is used to determine the reliability and validity of the output. The results show that the new integrity algorithm has the capability to support a various types of location-based ITS services.Saudi Arabia Cultural Burea
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