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- W4322731602 abstract "Localization systems are crucial to every intelligent mobile system, especially to automated vehicles. Knowing the own position, together with sensor data and predefined information, is the basis for all driving decisions. Significant effort has been made up to this point to improve robustness and functionality of localization systems. Nonetheless, such safety critical components must not fail. Besides self-testing of a localization system another possibility is to externally observe it’s functionality. Possible failures might be error-prone pose measurements or timing issues. Although localization systems usually suffer from tiny inaccuracies, e.g. due to sensor noise and model assumptions, these do not affect the capabilities of the automated system in a critical way. Therefore, this paper presents a novel method for deriving the functionality of an arbitrary localization system by utilizing odometry data of the vehicle and comparing the pose and time differences to respective parameterized probability distributions learned in advance from error-free mapping drives. Since the performance of the localization system is position-dependent the method utilizes a grid map and calculates the best fitting probability distribution for each cell during the mapping phase. An evaluation of the proposed failure detection shows that it is capable of recognizing errors of the localization system." @default.
- W4322731602 created "2023-03-03" @default.
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- W4322731602 date "2022-09-22" @default.
- W4322731602 modified "2023-10-01" @default.
- W4322731602 title "Modeling Localization Uncertainty for Enhanced Robustness of Automated Vehicles" @default.
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- W4322731602 doi "https://doi.org/10.1109/iccp56966.2022.10053961" @default.
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