  In the driving scene, the road agents usually conduct frequent interactions
and intention understanding of the surroundings. Ego-agent (each road agent
itself) predicts what behavior will be engaged by other road users all the time
and expects a shared and consistent understanding for safe movement. Behavioral
Intention Prediction (BIP) simulates such a human consideration process and
fulfills the early prediction of specific behaviors. Similar to other
prediction tasks, such as trajectory prediction, data-driven deep learning
methods have taken the primary pipeline in research. The rapid development of
BIP inevitably leads to new issues and challenges. To catalyze future research,
this work provides a comprehensive review of BIP from the available datasets,
key factors and challenges, pedestrian-centric and vehicle-centric BIP
approaches, and BIP-aware applications. Based on the investigation, data-driven
deep learning approaches have become the primary pipelines. The behavioral
intention types are still monotonous in most current datasets and methods
(e.g., Crossing (C) and Not Crossing (NC) for pedestrians and Lane Changing
(LC) for vehicles) in this field. In addition, for the safe-critical scenarios
(e.g., near-crashing situations), current research is limited. Through this
investigation, we identify open issues in behavioral intention prediction and
suggest possible insights for future research.
