Abstract
Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence (AI) techniques. Accurate behavior prediction of surrounding traffic actors is essential for the safe and secure navigation of the intelligent vehicle. Minor misbehavior of these vehicles on the busy roads may lead to an accident. Due to this, there is a need for vehicle behavior research work in today’s era. This research article reviews traffic actors’ behavior prediction techniques for intelligent vehicles to perceive, infer, and anticipate other vehicles’ intentions and future actions. It identifies the key strategies and methods for AI, emerging trends, datasets, and ongoing research issues in these fields. As per the authors’ knowledge, this is the first systematic literature review dedicated to the vehicle behavior study examining existing academic literature published by peer review venues between 2011 and 2021. A systematic review was undertaken to examine these papers, and five primary research questions have been addressed. The findings show that using sophisticated input representation that includes traffic rules and road geometry, artificial intelligence-based solutions applied to behavior prediction of traffic actors for intelligent vehicles have shown promising success, particularly in complex driving scenarios. Finally, the paper summarizes the most widely used approaches in behavior prediction of traffic actors for intelligent vehicles, which the authors believe serves as a foundation for future research in behavior prediction of surrounding traffic actors for secure and accurate intelligent vehicle navigation.
Highlights
Many companies, such as Waymo and Lyft, are working on intelligent vehicle technology for various vehicles
State and track history of SAs, similar to Target Actors (TAs), can be divides the space around the ego vehicle (EV) into equivalent used as input for the prediction model to improve performance cells that reflect the state of occupation of nearby regions, i.e., in predicting the behavior of TA
The main findings of this study are the identifying challenges in input representation that affect the performance of the behavior prediction model
Summary
Many companies, such as Waymo and Lyft, are working on intelligent vehicle technology for various vehicles. The authors discussed the state-of-thevariant of RNN, the long short-term memory (LSTM) model, art research developments and challenges to overcome has been popularly used to predict the behavior of pedestrians towards finding solutions closer to the human ability to predict and surrounding vehicles [6], [7]. It discusses emerging cutting-edge technology and the obstacles that Pedestrian behavior prediction in the urban Review must be overcome to find a better solution similar to the human level scenario for intelligent vehicle performance to anticipate and interpret pedestrian behavior This challenge requires high response time, accuracy, and precision in the real world. Behavior prediction of traffic actors in the context of intelligent vehicles
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