Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection

TL;DR

This paper introduces a data-driven approach using Dynamic Bayesian Networks and Markov Jump Particle Filters to enable autonomous vehicles to detect abnormalities and develop self-awareness in complex environments.

Contribution

It presents a novel method combining DBNs and MJPFs for abnormality detection and collective awareness in autonomous vehicles, advancing beyond manual programming approaches.

Findings

Effective abnormality detection demonstrated on real vehicle datasets

Dynamic Bayesian Models accurately predict vehicle states

Proposed models enable cooperative anomaly detection

Abstract

The evolution of Intelligent Transportation Systems in recent times necessitates the development of self-awareness in agents. Before the intensive use of Machine Learning, the detection of abnormalities was manually programmed by checking every variable and creating huge nested conditions that are very difficult to track. This paper aims to introduce a novel method to develop self-awareness in autonomous vehicles that mainly focuses on detecting abnormal situations around the considered agents. Multi-sensory time-series data from the vehicles are used to develop the data-driven Dynamic Bayesian Network (DBN) models used for future state prediction and the detection of dynamic abnormalities. Moreover, an initial level collective awareness model that can perform joint anomaly detection in co-operative tasks is proposed. The GNG algorithm learns the DBN models' discrete node variables; probabilistic transition links connect the node variables. A Markov Jump Particle Filter (MJPF) is applied to predict future states and detect when the vehicle is potentially misbehaving using learned DBNs as filter parameters. In this paper, datasets from real experiments of autonomous vehicles performing various tasks used to learn and test a set of switching DBN models.