Portal de Periódicos da CAPES

Correlation Analysis of In-Vehicle Sensors Data and Driver Signals in Identifying Driving and Driver Behaviors

2022; RELX Group (Netherlands); Linguagem: Inglês

10.2139/ssrn.4183382

1556-5068

Lucas Vacilotto Bonfati, José Jair Alves Mendes, Sérgio Luiz Stevan,

Color perception and design

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Correlation Analysis of In-Vehicle Sensors Data and Driver Signals in Identifying Driving and Driver Behaviors BSPC-D-22-01578 21 Pages Posted: 6 Aug 2022 See all articles by L. V. BonfatiL. V. BonfatiFederal Technological University of ParanáJose Jair Alves Mendes JuniorFederal Technological University of ParanáSergio L. Stevan Jr.Federal Technological University of Parana (UTFPR) Abstract Today's cars have dozens of sensors to monitor vehicle performance through different systems, most of which communicate via vehicular networks (CAN). Many of these sensors can be used for applications other than the original ones, such as improving the driver experience or creating new safety tools. An example might be monitoring variables that can describe the driver's behavior.Interactions with the pedals, speed, steering wheel, among other signals, carry driving characteristics. However, not always all variables related to these interactions are available in all vehicles, for example, the excursion of the brake pedal.Using an acquisition module, data from the in-vehicle sensors were obtained from the CAN-BUS, the brake pedal (externally instrumented), and the driver's signals (instrumented with an inertial sensor and electromyography of his leg).Different sets of sensors were evaluated to analyze the performance of three classifiers when analyzing the driver's driving mode. It was found that there are superior results in the classification of identity or behavior when driver signals are included.When the vehicle and driver attributes were used, hits above 0.93 were obtained in the identification of behavior and 0.96 in the identification of the driver; without driver signals, accuracy was greater than 0.80 in identifying behavior. The results show a good correlation between vehicle data and data obtained from the driver, suggesting that further studies may be promising to improve the accuracy of rates based exclusively on vehicle characteristics, both for behavior identification and driver identification. Keywords: Driver behavior analysis, Feature Extraction, surface electromyography, CAN, Classification Suggested Citation: Suggested Citation Bonfati, L. V. and Mendes Junior, Jose Jair Alves and Stevan Jr., Sergio L., Correlation Analysis of In-Vehicle Sensors Data and Driver Signals in Identifying Driving and Driver Behaviors. BSPC-D-22-01578, Available at SSRN: https://ssrn.com/abstract=4183382 L. V. Bonfati Federal Technological University of Paraná ( email ) Campo MourãoBrazil Jose Jair Alves Mendes Junior Federal Technological University of Paraná ( email ) Campo MourãoBrazil Sergio L. Stevan Jr. (Contact Author) Federal Technological University of Parana (UTFPR) ( email ) Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 2 Abstract Views 6 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.539 seconds