Abstract
The evolution of Intelligent Transportation System in recent times necessitates the development of self-driving agents: the self-awareness consciousness. This paper aims to introduce a novel method to detect abnormalities based on internal cross-correlation parameters of the vehicle. Before the implementation of Machine Learning, the detection of abnormalities were manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. Nowadays, it is possible to train a Dynamic Bayesian Network (DBN) model to automatically evaluate and detect when the vehicle is potentially misbehaving. In this paper, different scenarios have been set in order to train and test a switching DBN for Perimeter Monitoring Task using a semantic segmentation for the DBN model and Hellinger Distance metric for abnormality measurements.
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Supported by SEGVAUTO 4.0 P2018/EMT-4362) and CICYT projects (TRA2015-63708-R and TRA2016-78886-C3-1-R).
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Kanapram, D., Marin-Plaza, P., Marcenaro, L., Martin, D., de la Escalera, A., Regazzoni, C. (2020). Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection. In: Silva, M., LuÃs Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-35990-4_18
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