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.

翻译：在驾驶场景中，道路智能体通常进行频繁的交互以及对周围环境的意图理解。自车（每个道路智能体自身）需要持续预测其他道路使用者的行为意图，并期望形成共享且一致的理解以确保安全行驶。行为意图预测（Behavioral Intention Prediction, BIP）模拟了这种人类思考过程，并实现了对特定行为的早期预测。与轨迹预测等其他预测任务类似，数据驱动的深度学习方法已成为该研究领域的主要技术路线。BIP的快速发展不可避免地引发了新的问题与挑战。为促进未来研究，本文从现有数据集、关键因素与挑战、以行人为中心和以车辆为中心的BIP方法，以及BIP感知应用等方面对BIP进行了全面综述。研究表明，数据驱动的深度学习方法已成为主要技术路线。目前该领域多数数据集与方法中的行为意图类型仍较为单一（例如，行人的"穿越"（Crossing, C）与"不穿越"（Not Crossing, NC），以及车辆的"车道变更"（Lane Changing, LC））。此外，针对安全关键场景（如接近碰撞的情况），当前研究仍十分有限。通过本次调研，我们指出了行为意图预测领域中的未解决问题，并为未来研究提出了可能的见解。