Advanced Driver Assistance Systems (ADAS) are designed to prevent collisions, identify the condition of drivers while operating vehicles, and provide additional information to enhance drivers’ awareness of potential hazards on the road. Today, ADAS are capable of predicting drivers’ actions several seconds in advance, preparing for potential future hazards to prevent accidents or reduce injuries to occupants. Most previous works have achieved prediction results by analyzing and processing a vast amount of driving data from multiple drivers, based on the macro intention preferences of multiple drivers and external environmental features, collectively referred to as the generalized intention prediction network. This network utilizes extensive driving data to predict the common driving intentions of the overall driving population, without considering individualized driving styles. However, according to our research, different drivers exhibit distinct latent preferences in real-world driving scenarios. The generalized intention prediction network is influenced by these latent preferences, resulting in poor generalization capabilities and inaccurate predictions across different drivers. In this study, we propose a individualized driver intention prediction network. Based on Inverse Reinforcement Learning (IRL), it extracts individualized driving intention feature preferences that influence driving intentions from the driver’s historical behavior to improve generalized prediction results and achieve individualized driving intention prediction. We demonstrate that preferences vary among different drivers in the driving domain, leading to biases in model predictions. Upon experimental validation, the method we have proposed demonstrates remarkable efficacy on both the Brain4Cars and IESDD datasets, thereby showcasing its enhanced applicability in real-world scenarios.

Multi-agent behavior and intention analysis has been applied in many aspects of our daily lives such as driving maneuver anticipation and assistive driving perception. However, the research in this field suffers from the lack of publicly available datasets, which is mainly caused by the low availability and high complexity of the multi-agent behavior and intention data. In this paper, we propose MBI, a dataset that contains five categories of agents in eight scenarios based on an open-source simulated platform called HarFang3D DogFight SandBox. In MBI, five types of behaviors and five types of intentions are collected. Additionally, we benchmark different Graph Neural Networks (GNNs) on the proposed dataset to test their performance on the tasks of analyzing different behaviors and intentions in multi-agent systems. We also propose a new method called D2RGAT and find it can achieve the best results on MBI.