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A General Goal Point Model for Anticipation of Vehicle Motions for Autonomous Driving

Author
Zhou, Yichen
Abstract
In the field of autonomous driving, anticipation of the dynamic environment is of great importance for the ego vehicle to make decisions and plan future paths in order to ensure safety and efficiency. This thesis presents a general goal point model for making predictions of vehicle motions around a moving ego vehicle. One or multiple goal points are selected based on a road graph and other environmental information. Vehicle predictions are then initialized from a probabilistic tracker and propagated via a motion model toward the goal point. This anticipation model is validated on a real-time dataset and evaluated against an open-loop, purely kinematic baseline model, demonstrating its predictive performance over a 1.5-second window in various scenarios.
Date Issued
2019-05-30Subject
anticipation; autonomous driving; Robotics; Mechanical engineering
Committee Chair
Hooker, Giles J.
Committee Member
Joachims, Thorsten; Basu, Sumanta
Degree Discipline
Mechanical Engineering
Degree Name
M.S., Mechanical Engineering
Degree Level
Master of Science
Type
dissertation or thesis