Alle Publikationen
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2018
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(2018) : Emotionally Adaptive Driver Voice Alert System for Advanced Driver Assistance System (ADAS) Applications: 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT): Tirunelveli, India: IEEE, S. 509-512
DOI: https://doi.org/10.1109/ICSSIT.2018.8748541 Abstract: Human cognitive analysis catalyzes the innovations in Human Machine Interface (HMI) for a variety of applications. In an Automotive Advanced Driver Assistance System (ADAS), the continuous cognitive interaction of the driver with the assistance system plays a crucial role in enhancing the active safety system. Multiple ADAS functionalities uses a variety of driver alerts through visual, audio and vibrational means to provide a numerous safety alerts to the driver. The effectiveness of any alert system is measured through its success rate in mitigating the actions which are against the alert commands. The actions taken by the driver for the alerts depends heavily on the driver’s moods, which are responsible for driver’s perception in understanding the alerts. Even though the voice alerts are considered as the most effective form of human alerts, the static nature of the voice alerts makes them less effective in making the driver to understand the criticality of the alerts when his moods are abnormal or having a reduced driving concentration levels. An adaptive voice alert system with a cognitive driver synchronization makes the alert penetration successful when the driver’s moods are abnormal or having a reduced driving concentration levels. Here in this paper the adaptive voice alert system is designed using the driver’s emotional cognitive features. The emotionally adaptive voice alert system changes the voice alerts as according to the moods of the driver, which are measured by Deep Learning based Emotion Recognition System. The adaptive voice alert system makes the voice enabled HMI effective which improves the vehicle safety.
Keywords: active safety system, adaptive driver voice alert system, Adaptive systems, Advanced Driver Assistance System (ADAS), advanced driver assistance system applications, Advanced driver assistance systems, alert commands, alert penetration successful, automotive advanced driver assistance system, Cognition, cognitive driver synchronization, Convolutional Neural Network (CNN), driver alerts, driver information systems, emotion recognition, emotion recognition system, Emotion Recognition System (ERS), emotionally adaptive voice alert system, human alerts, human cognitive analysis, Human Computer Interaction, Human Machine Interface (HMI), ieee xplore, Künstliche Intelligenz, learning (artificial intelligence), Monitoring, natural language interfaces, safety alerts, Vehicles, voice alerts 2016
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(2016) : Towards ethical robots: Revisiting Braitenberg’s vehicles: 2016 SAI Computing Conference (SAI): London, United Kingdom: IEEE, S. 469-477
DOI: https://doi.org/10.1109/SAI.2016.7556023 Abstract: The development of software and machines capable of making ethical judgements is a topic of great interest with both the research communities and the public. Debates over the possibility and practicality of such systems have only intensified with the increased use of robotics in the military arena and the ubiquity of AI in commercial products. Modern innovations, such as the driverless car, will likely make artificial ethical agents a legal necessity. As a research field, it has received relatively little attention compared to other, more traditional, AI problems. In this paper, we propose a bottom-up reactive system that provides one possible solution. We will begin by describing the motivation to this work: the development of artificial ethical agents could both mitigate some fears about the future of autonomous AI, and providing insight into human moral reasoning. We then explore the related work, including the current attempts at simulating ethics. We describe our novel approach to ethical simulation, Vessels; a Braitenberg Vehicle inspired reactive agent approach. We, then, demonstrate how Vessels can be configured to simulate both Egoism and Altruism, comparing our simulations to the normative theory.
2013
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(2013) : Let the machines do. How intelligent is Artificial Intelligence?: 2013 36th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO): Opatija, Croatia: IEEE, S. 947-952
Abstract: “Intelligent” systems are present around us. Such machines are not only tools in our hands, they are able to make decisions and perform actions directly in the real world or in the artificial worlds of the internet. But even in the latter case, their decisions have consequences to our life. Many of them still act as “assistance systems”, leaving the final decision to the human user. The trend goes to more autonomy of the machines, even in critical situations when humans become overloaded by complexity. Additionally, humans are more and more willing to accept the proposals of the machines. But is this technique mature enough to guide or even to replace human decision-making? Especially, perception appears to be a hard problem for technical equipment. How accurate, how safe can decisions be in the case of incomplete and unreliable data? The paper gives some overview about recent developments in Artificial Intelligence and Robotics, their capabilities and serious problems from a general point of view.
2011
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(2011) : A novel real-time emotion detection system from audio streams based on Bayesian Quadratic Discriminate Classifier for ADAS: Proceedings of the Joint INDS’11 ISTET’11: Klagenfurt, Austria: IEEE, S. 1-5
DOI: https://doi.org/10.1109/INDS.2011.6024783 Abstract: This paper presents a real-time emotion recognition concept of voice streams. A comprehensive solution based on Bayesian Quadratic Discriminate Classifier(QDC) is developed. The developed system supports Advanced Driver Assistance Systems (ADAS) to detect the mood of the driver based on the fact that aggressive behavior on road leads to traffic accidents. We use only 12 features to classify between 5 different classes of emotions. We illustrate that the extracted emotion features are highly overlapped and how each emotion class is effecting the recognition ratio. Finally, we show that the Bayesian Quadratic Discriminate Classifier is an appropriate solution for emotion detection systems, where a real-time detection is deeply needed with a low number of features.
Keywords: ADAS, Advanced driver assistance systems, Angemessen(heit) (von Technik), audio signal processing, audio streaming, audio streams, Bayes methods, Bayesian methods, Bayesian quadratic discriminate classifier, driver information systems, emotion feature extraction, emotion recognition, Feature extraction, ieee xplore, real-time emotion detection system, real-time emotion recognition concept, road accidents, speech, speech recognition, Support vector machines, traffic accidents, Vehicles, voice streams
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