388,094 research outputs found

    Bayesian Programming Multi-Target Tracking: an Automotive Application

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    A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. In particular, target tracking is still challenging in urban trafc situations, because of the large number of rapidly maneuvering targets. The goal of this paper is to present an original way to perform target position and velocity, based on the occupancy grid framework. The main interest of this method is to avoid the decision problem of classical multi-target tracking algorithms. Obtained occupancy grids are combined with danger estimation to perform an elementary task of obstacle avoidance with an electric car

    Advanced Driver Assistance and Safety Warning System for Car Driving

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    The quick advance in technology and infrastructure has made of lives more easy. Most accidents are occurring by making mistakes like rash driving, driving the vehicles without noticing traffic signs. In this work, efficient driver assistance system is developed by making use of ultrasonic sensors, MEMS, RF, GPS and GSM modules. An ultrasonic sensor is used to detect the obstacle in front of the vehicle and the vehicle gets stopped immediately to avoid the accident, alert the diver regarding the blind spots. Intimate the driver about the traffic signs (School ahead, Speed limit) to prevent the accident to occur. DOI: 10.17762/ijritcc2321-8169.150713

    Lateral control assistance and driver behavior in emergency situations

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    Advanced Driver Assistance Systems (ADAS) are designed to help drivers improve driving safety. However, automation modifying the way drivers interact with their vehicle, it is important to avoid negative safety impacts. In particular, the change in drivers’ behavior introduced by ADAS in situations they are not designed for, should be carefully examined. We carried out an experiment on a driving simulator to study drivers’ reaction in an obstacle avoidance situation, when using a lateral control assistance system. A detailed analysis of the avoidance maneuver is presented. Results show that assisted and non-assisted drivers equally succeeded in avoiding the obstacle. However, further analyses tend to show an influence of the assistance system on drivers’ first reaction

    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

    Using Bayesian Programming for Multisensor Multi-Target Tracking in Automative Applications

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    A prerequisite to the design of future Advanced Driver Assistance Systems for cars is a sensing system providing all the information required for high-level driving assistance tasks. Carsense is a European project whose purpose is to develop such a new sensing system. It will combine different sensors (laser, radar and video) and will rely on the fusion of the information coming from these sensors in order to achieve better accuracy, robustness and an increase of the information content. This paper demonstrates the interest of using probabilistic reasoning techniques to address this challenging multi-sensor data fusion problem. The approach used is called Bayesian Programming. It is a general approach based on an implementation of the Bayesian theory. It was introduced rst to design robot control programs but its scope of application is much broader and it can be used whenever one has to deal with problems involving uncertain or incomplete knowledge
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