Self-awareness in Intelligent Vehicles: Experience Based Abnormality Detection

TL;DR

This paper presents a novel abnormality detection method for intelligent vehicles using a switching Dynamic Bayesian Network trained on semantic segmentation data and Hellinger Distance, enhancing vehicle self-awareness.

Contribution

It introduces a switching DBN approach for abnormality detection in intelligent vehicles, moving beyond manual rule-based systems to automated probabilistic modeling.

Findings

Effective detection of vehicle abnormalities in various scenarios

Switching DBN outperforms traditional rule-based methods

Utilization of Hellinger Distance improves abnormality measurement

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.