Sensor Fault Detection and Diagnosis for autonomous vehicles

Miguel Realpe,Ljubo Vlacic,Boris Vintimilla,J Zhou,O Adiguzel

doi:10.1051/matecconf/20153004003

Miguel Realpe, Ljubo Vlacic + Show 3 more

Open Access

https://doi.org/10.1051/matecconf/20153004003

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Abstract

In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor.

Highlights

Many autonomous vehicles are currently being tested on public roads in order to demonstrate safe and reliable operation in real world situations
The proposed architecture has been tested using a sequence of 270 images from the KITTI dataset in a Core i5 CPU at 3.10 GHz
The support vector machines (SVM) models were trained using a subset of 25 representative images from the 270 testing set

Summary

Introduction

Many autonomous vehicles are currently being tested on public roads in order to demonstrate safe and reliable operation in real world situations. A federated sensor data fusion architecture is proposed in order to provide fault tolerance to one of three redundant sensors of an autonomous vehicle’s perception system. In this model, the perception layer provides state estimations of the objects; the decision application layer predictsfuture situations and deduces output of potential manoeuvres; and the action/HMI layer collects and provides information to the user. The system has been divided into different modules: one object detection for each sensor type, one local fusion for each support sensor, one master fusion, a tracking module and the Fault Detection and Diagnosis [FDD] module

Model Description

Object Detection and Local Fusion

Master Fusion

Fault Detection and Diagnosis

Experimental Results

Conclusions