Carlos Conejo , Vicenç Puig , Bernardo Morcego , Francisco Navas , Vicente Milanés
{"title":"Enhancing safety in autonomous vehicles using zonotopic LPV-EKF for fault detection and isolation in state estimation","authors":"Carlos Conejo , Vicenç Puig , Bernardo Morcego , Francisco Navas , Vicente Milanés","doi":"10.1016/j.conengprac.2024.106192","DOIUrl":null,"url":null,"abstract":"<div><div>In this paper, a solution is presented to address the sensor fault detection and isolation (FDI) problem in state estimation for autonomous vehicles (AVs). The primary impetus for autonomous driving lies in its potential to ensure vehicle safety, a goal that requires an accurate determination of location, heading, and speed. Although sensors can directly obtain these measurements, they are often affected by noise and disturbances with unknown but bounded (UBB) distributions. To mitigate these effects, state estimation techniques are commonly employed, leveraging sensor fusion. This work aims to design an FDI methodology that continuously evaluates the accuracy of the state estimation algorithm in an AV. In order to achieve this goal, various observation techniques for robust FDI are compared, including a novel approach of EKF formulated within the LPV framework, named LPV-EKF. A zonotopic LPV-EKF observer is implemented to perform FDI on both state estimation inputs and outputs, considering an UBB noise distribution. The proposed methodology for the identification of anomalies is optimised to minimise the detection time in real world scenarios. The experimental results for FDI, collected from an autonomous Renault Zoe (SAE Level 3), are analysed and discussed.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"156 ","pages":"Article 106192"},"PeriodicalIF":5.4000,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967066124003514","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a solution is presented to address the sensor fault detection and isolation (FDI) problem in state estimation for autonomous vehicles (AVs). The primary impetus for autonomous driving lies in its potential to ensure vehicle safety, a goal that requires an accurate determination of location, heading, and speed. Although sensors can directly obtain these measurements, they are often affected by noise and disturbances with unknown but bounded (UBB) distributions. To mitigate these effects, state estimation techniques are commonly employed, leveraging sensor fusion. This work aims to design an FDI methodology that continuously evaluates the accuracy of the state estimation algorithm in an AV. In order to achieve this goal, various observation techniques for robust FDI are compared, including a novel approach of EKF formulated within the LPV framework, named LPV-EKF. A zonotopic LPV-EKF observer is implemented to perform FDI on both state estimation inputs and outputs, considering an UBB noise distribution. The proposed methodology for the identification of anomalies is optimised to minimise the detection time in real world scenarios. The experimental results for FDI, collected from an autonomous Renault Zoe (SAE Level 3), are analysed and discussed.
期刊介绍:
Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper.
The scope of Control Engineering Practice matches the activities of IFAC.
Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.