Behavioral Intention Prediction in Driving Scenes: A Survey

In driving scenes, road agents often engage in frequent interaction and strive to understand their 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. To achieve this, Behavioral Intention Prediction (BIP) simulates such a human consideration process to anticipate specific behaviors, and 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, challenges, pedestrian-centric and vehicle-centric BIP approaches, and BIP-aware applications. The investigation reveals that data-driven deep learning approaches have become the primary pipelines, while the behavioral intention types are still limited 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, current research on BIP in safe-critical scenarios (e.g., near-crashing situations) is limited. Through this investigation, we identify open issues in behavioral intention prediction and suggest possible insights for future research.