Selective attention for detection and tracking of road-networks in autonomous driving

Unterholzner, Alois; Wuensche, Hans-Joachim

doi:10.1109/ivs.2013.6629482

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…The strategies and frameworks for object localization or tracking are also proposed depending on the Kalman filter [197] and deep convolutional neural networks [198]. Furthermore, some other approaches are designed by use of the trade-off between camera orientations prediction and monitoring techniques [199], [200].…”

Section: B Autonomous Drivingmentioning

confidence: 99%

Importance of Small Probability Events in Big Data: Information Measures, Applications, and Challenges

et al. 2019

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In many applications (e.g., anomaly detection and security systems) of smart cities, rare events dominate the importance of the total information on big data collected by the Internet of Things (IoT). That is, it is pretty crucial to explore the valuable information associated with the rare events involved in minority subsets of the voluminous amounts of data. To do so, how to effectively measure the information with the importance of the small probability events from the perspective of information theory is a fundamental question. This paper first makes a survey of some theories and models with respect to importance measures and investigates the relationship between subjective or semantic importance and rare events in big data. Moreover, some applications for message processing and data analysis are discussed in the viewpoint of information measures. In addition, based on rare events detection, some open challenges related to information measures, such as smart cities, autonomous driving, and anomaly detection in the IoT, are introduced which can be considered as future research directions.

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Section: B Autonomous Drivingmentioning

confidence: 99%

Importance of Small Probability Events in Big Data: Information Measures, Applications, and Challenges

et al. 2019

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“…• Multiple datasets analysis Automatic driving [189], [190], [191], [192], [193], [194], [195], [196], [197], [198], [199], [200], [201], [202], [203], [204], [205], [206] [207], [208], [209] • Obstacles detection…”

Section: Work Area Related Work Key Pointsmentioning

confidence: 99%

“…The strategies and frameworks for object localization or tracking are also proposed depending on the Kalman filter [198] and deep convolutional neural networks [199]. Furthermore, some other approaches are designed by use of the trade-off between camera orientations prediction and monitoring techniques [200], [201].…”

Section: B Autonomous Drivingmentioning

confidence: 99%

Importance of Small Probability Events in Big Data: Information Measures, Applications, and Challenges

Sheng

Liu

Wan

et al. 2019

Preprint

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In many applications (e.g., anomaly detection and security systems) of smart cities, rare events dominate the importance of the total information of big data collected by Internet of Things (IoTs). That is, it is pretty crucial to explore the valuable information associated with the rare events involved in minority subsets of the voluminous amounts of data. To do so, how to effectively measure the information with importance of the small probability events from the perspective of information theory is a fundamental question. This paper first makes a survey of some theories and models with respect to importance measures and investigates the relationship between subjective or semantic importance and rare events in big data. Moreover, some applications for message processing and data analysis are discussed in the viewpoint of information measures. In addition, based on rare events detection, some open challenges related to information measures, such as smart cities, autonomous driving, and anomaly detection in IoTs, are introduced which can be considered as future research directions.

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“…The MuCAR-3 uses a combination of mainly a high-definition 360 • laser scanner and camera sensors to perceive the environment and carry out autonomous dirt road following [30,[188][189][190][191] and vehicle convoy applications [111-113, 175, 188]. In order to improve perception in such environments, an active perception system was developed which actively focuses a camera system towards the most important area of the environment [298], thereby effectively increasing the field-of-view without requiring the installation of more sensors.…”

Section: Recent Attempts At Autonomous Drivingmentioning

confidence: 99%

Object-Level Fusion for Surround Environment Perception in Automated Driving Applications

Aeberhard¹

2017

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Driver assistance systems have increasingly relied on more sensors for new functions. As advanced driver assistance system continue to improve towards automated driving, new methods are required for processing the data in an efficient and economical manner from the sensors for such complex systems. In this thesis, an environment model approach for the detection of dynamic objects is presented in order to realize an effective method for sensor data fusion. A scalable high-level fusion architecture is developed for fusing object data from several sensors in a single system. The developed high-level sensor data fusion architecture and its algorithms are evaluated using a prototype vehicle equipped with 12 sensors for surround environment perception. The work presented in this thesis has been extensively used in several research projects as the dynamic object detection platform for automated driving applications on highways in real traffic. Contents Abbreviations VIII List of Symbols X Abs...

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Selective attention for detection and tracking of road-networks in autonomous driving

Cited by 4 publications

References 11 publications

Importance of Small Probability Events in Big Data: Information Measures, Applications, and Challenges

Importance of Small Probability Events in Big Data: Information Measures, Applications, and Challenges

Importance of Small Probability Events in Big Data: Information Measures, Applications, and Challenges

Object-Level Fusion for Surround Environment Perception in Automated Driving Applications

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