  Autonomous Vehicles (AVs) have emerged as a promising solution by replacing
human drivers with advanced computer-aided decision-making systems. However,
for AVs to effectively navigate the road, they must possess the capability to
predict the future behavior of nearby traffic participants, similar to the
predictive driving abilities of human drivers. Building upon existing
literature is crucial to advance the field and develop a comprehensive
understanding of trajectory prediction methods in the context of automated
driving. To address this need, we have undertaken a comprehensive review that
focuses on trajectory prediction methods for AVs, with a particular emphasis on
machine learning techniques including deep learning and reinforcement
learning-based approaches. We have extensively examined over two hundred
studies related to trajectory prediction in the context of AVs. The paper
begins with an introduction to the general problem of predicting vehicle
trajectories and provides an overview of the key concepts and terminology used
throughout. After providing a brief overview of conventional methods, this
review conducts a comprehensive evaluation of several deep learning-based
techniques. Each method is summarized briefly, accompanied by a detailed
analysis of its strengths and weaknesses. The discussion further extends to
reinforcement learning-based methods. This article also examines the various
datasets and evaluation metrics that are commonly used in trajectory prediction
tasks. Encouraging an unbiased and objective discussion, we compare two major
learning processes, considering specific functional features. By identifying
challenges in the existing literature and outlining potential research
directions, this review significantly contributes to the advancement of
knowledge in the domain of AV trajectory prediction.
