Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection
This addresses the need for self-awareness in intelligent vehicles to improve safety and reliability, though it appears incremental as it builds on existing DBN techniques.
The paper tackles the problem of detecting abnormalities in self-driving vehicles by proposing a method using a switching Dynamic Bayesian Network (DBN) with semantic segmentation and Hellinger Distance, achieving automated detection that replaces manual programming.
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.