Driver Behavior Modeling for Autonomous Vehicle Motion Planning

مرکز و کتابخانه مطالعات اسلامی به زبان های اروپایی

منو

" Driver Behavior Modeling for Autonomous Vehicle Motion Planning "
Ramyar, Saina Homaifar, Abdollah

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1106334
Doc. No	:	TLpq2385624822
Main Entry	:	Homaifar, Abdollah
	:	Ramyar, Saina
Title & Author	:	Driver Behavior Modeling for Autonomous Vehicle Motion Planning\ Ramyar, SainaHomaifar, Abdollah
College	:	North Carolina Agricultural and Technical State University
Date	:	2019
student score	:	2019
Degree	:	Ph.D.
Page No	:	108
Abstract	:	The advanced driving assistance systems (ADAS) such as lane departure warning and collision avoidance have great potentials in improving traffic safety. In order for the ADAS to be able to detect and prevent an accident, it is required to predict other vehicles' actions and plan the subject vehicle's motion accordingly. Due to the complexity of human-vehicle interaction, obtaining an explicit model for analyzing the drivers’ behaviors is difficult. Instead, models are developed for various driver decisions and driving scenarios which are integrated together. In this dissertation, machine learning models are developed to represent human driver behaviors both on urban roads and highways. A fuzzy clustering approach is proposed to predict the driver's actions at intersections. Moreover, an anomaly detection technique is used to identify potential abnormalities that may lead to various hazards during the process of a lane change. In addition to driver models developed for safe trajectory generation, a personalized driver model is proposed, which is learned through demonstration and performs according to the driver's preference. Furthermore, a cooperative car following model for platoons is proposed which estimates the preceding vehicle's acceleration in case of communication failure and enables the platoon to maintain a relatively small inter-vehicle gap and remain string stable. All the models in this dissertation are based on naturalistic driving behavior and have been tested in various scenarios. The simulation results show the high accuracy of the proposed models and validate their applicability for autonomous motion planning.
Subject	:	Artificial intelligence
	:	Robotics
	:	Transportation