Abstract
The influence of the driver's physiological and psychological characteristics on traffic safety is mainly represented as driver's tendency. Previous research about the driver's tendency mostly focused on the influence on traffic safety and the driver's psychological characteristics from a relative static and macroscopic perspective. In this paper, the Multi-Sensor Real-time Information Gathering Systems and the car-following experiments are designed to collect dynamic information about driver's behavior, vehicle state, traffic environment, etc., to compute driver's tendency. The data is divided into groups and ran through BP Neural Networks to obtain the correct classification rate for estimation. The characteristics of driver's tendency are extracted from a great number of samples through the Discrete Particle Swarm Optimization Method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
