Driving Intention Identification Model Based on Long and Short-Term Memory Network
The study of driver’s driving intention is of great significance to improve vehicle safety warning technology, auxiliary driving technology and optimization of vehicle control strategy. By analyzing the driving environment data collected by the Internet of vehicles monitoring platform, the driving environment perception model based on the long-term memory network (LSTM) is established to judge the driving environment. After that, the symbolized environmental result combined with vehicle state parameters collected by the sensors are used as input to the driving intention identification model based on LSTM. Then the dynamic driving behavior and driving intention of the driver are analyzed. The results show that, based on the dynamic model LSTM, the driving intention identification model considering the driving environment produce a higher accuracy.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784483053
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Supplemental Notes:
- © 2020 American Society of Civil Engineers.
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Corporate Authors:
American Society of Civil Engineers
1801 Alexander Bell Drive
Reston, VA United States 20191-4400 -
Authors:
- Yuan, Tian
- Chai, Hua
- Ma, Ke-Xin
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Conference:
- 20th COTA International Conference of Transportation Professionals
- Location: Xi’an , China
- Date: 2020-8-14 to 2020-8-16
- Publication Date: 2020
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 2392-2402
- Monograph Title: CICTP 2020: Transportation Evolution Impacting Future Mobility
Subject/Index Terms
- TRT Terms: Behavior; Driver monitoring; Driver performance; Dynamic models; Intelligent vehicles; Technological innovations; Warning systems
- Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01768156
- Record Type: Publication
- ISBN: 9780784483053
- Files: TRIS, ASCE
- Created Date: Mar 25 2021 9:35AM