Driving-PASS: An Automatic Driving Performance Assessment System for Stroke Drivers Based on ANN and SVM

Sanghoon Jeon, Joonwoo Son, Myoungouk Park, Bawul Kim, Sang Hyuk Son, Yongsoon Eun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Although many stroke survivors are not fully capable of driving, they drive again without any formal assessment due to an absence of valid screening tools. This leads to an elevated risk of accidents. Although an on-road test is considered a standard assessment method for items relevant to actual driving, it may be dangerous to evaluate all stroke drivers with the on-road test. For safe pre-screening of unsuitable stroke drivers, we propose an automatic Driving Performance Assessment System for Stroke drivers (Driving-PASS). Driving-PASS aims to provide not only information about problematic driving assessment items but also a decision about fitness to drive. The problematic driving items are classified by abnormal classifiers while the decision item is determined by a decision classifier in Driving-PASS. For designing the system, we firstly propose a subjective assessment method consisting of ten assessment items and one decision item. And then, we propose an automated method of the subjective assessment method with a machine learning approach (i.e., ANN and SVM) by using assessment criteria from five expert's judgments. Evaluation results demonstrate that Driving-PASS automatically assess not only the ten assessment items (total average Accuracy of 90% and F1-score of 88%) but also the decision item (Accuracy of 93% and F1-score of 92%). We expect Driving-PASS provides analytical assessment results that can be used in driving rehabilitation programs and contributes to reducing the risk of vehicle accidents by pre-screening unsuitable stroke drivers with high accuracy and reliability.

Original languageEnglish
Title of host publication2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1367-1373
Number of pages7
ISBN (Electronic)9781538695821
DOIs
StatePublished - 18 Dec 2018
Event15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018 - Singapore, Singapore
Duration: 18 Nov 201821 Nov 2018

Publication series

Name2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018

Conference

Conference15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018
Country/TerritorySingapore
CitySingapore
Period18/11/1821/11/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

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