In the driving scene, the road participants usually show frequent interaction
and intention understanding with the surrounding. Ego-agent (each road
participant itself) conducts the prediction of what behavior will be done by
other road users all the time and expects a shared and consistent
understanding. For instance, we need to predict the next movement of other road
users and expect a consistent joint action to avoid unexpected accident.
Behavioral Intention Prediction (BIP) is to simulate such a human consideration
process and fulfill the beginning time prediction of specific behaviors. It
provides an earlier signal promptly than the specific behaviors for whether the
surrounding road participants will present specific behavior (crossing,
overtaking, and turning, etc.) in near future or not. More and more works in
BIP are based on deep learning models to take advantage of big data, and focus
on developing effective inference approaches (e.g., explainable inference,
cross-modality fusion, and simulation augmentation). Therefore, in this work,
we focus on BIP-conditioned prediction tasks, including trajectory prediction,
behavior prediction, and accident prediction and explore the differences among
various works in this field. Based on this investigation and the findings, we
discuss the open problems in behavioral intention prediction and propose future
research directions.

Behavioral Intention Prediction 技术在预测未来行为方面具有重要应用价值，本文探讨了 BIP-conditioned prediction 任务中各个方面的差异，并提出了未来研究方向。