  Scene understanding, defined as learning, extraction, and representation of
interactions among traffic elements, is one of the critical challenges toward
high-level autonomous driving (AD). Current scene understanding methods mainly
focus on one concrete single task, such as trajectory prediction and risk level
evaluation. Although they perform well on specific metrics, the generalization
ability is insufficient to adapt to the real traffic complexity and downstream
demand diversity. In this study, we propose PreGSU, a generalized pre-trained
scene understanding model based on graph attention network to learn the
universal interaction and reasoning of traffic scenes to support various
downstream tasks. After the feature engineering and sub-graph module, all
elements are embedded as nodes to form a dynamic weighted graph. Then, four
graph attention layers are applied to learn the relationships among agents and
lanes. In the pre-train phase, the understanding model is trained on two
self-supervised tasks: Virtual Interaction Force (VIF) modeling and Masked Road
Modeling (MRM). Based on the artificial potential field theory, VIF modeling
enables PreGSU to capture the agent-to-agent interactions while MRM extracts
agent-to-road connections. In the fine-tuning process, the pre-trained
parameters are loaded to derive detailed understanding outputs. We conduct
validation experiments on two downstream tasks, i.e., trajectory prediction in
urban scenario, and intention recognition in highway scenario, to verify the
generalized ability and understanding ability. Results show that compared with
the baselines, PreGSU achieves better accuracy on both tasks, indicating the
potential to be generalized to various scenes and targets. Ablation study shows
the effectiveness of pre-train task design.
