عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
شماره کتابشناسی ملی
شماره
TLpq2385624822
زبان اثر
زبان متن نوشتاري يا گفتاري و مانند آن
انگلیسی
عنوان و نام پديدآور
عنوان اصلي
Driver Behavior Modeling for Autonomous Vehicle Motion Planning
نام عام مواد
[Thesis]
نام نخستين پديدآور
Ramyar, Saina
نام ساير پديدآوران
Homaifar, Abdollah
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
North Carolina Agricultural and Technical State University
تاریخ نشرو بخش و غیره
2019
مشخصات ظاهری
نام خاص و کميت اثر
108
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
Ph.D.
کسي که مدرک را اعطا کرده
North Carolina Agricultural and Technical State University
امتياز متن
2019
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
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.
موضوع (اسم عام یاعبارت اسمی عام)
موضوع مستند نشده
Artificial intelligence
موضوع مستند نشده
Robotics
موضوع مستند نشده
Transportation
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )
مستند نام اشخاص تاييد نشده
Homaifar, Abdollah
مستند نام اشخاص تاييد نشده
Ramyar, Saina
دسترسی و محل الکترونیکی
نام الکترونيکي
مطالعه متن کتاب وضعیت انتشار
فرمت انتشار
p
اطلاعات رکورد کتابشناسی
نوع ماده
[Thesis]
کد کاربرگه
276903
اطلاعات دسترسی رکورد
سطح دسترسي
a
تكميل شده
Y
