## 1 Introduction  
Autonomous vehicles have become increasingly popular recently and are expected to remarkably improve transportation efficiency in innovative ways, which is enabled by a series of emerging technologies [1, 2]. The fact is that the premise of deploying autonomous vehicles is the need to ensure the absolute safety of all traffic participants. One of the alternatives is to have sufficient awareness of the other road sharers' movements for future periods. Evidently, if the future motion status of nearby agents can be accurately predicted, the autonomous vehicles would conduct progressive decision making to minimize risk and avoid potential collisions. However, due to the traffic environment's highly interdependent and dynamic characteristics, especially in urban driving scenes, accurate trajectory prediction is a challenging task for autonomous vehicles. To alleviate the matter raised above, multitudes of researchers in the field of autonomous vehicles have demonstrated diversified schemes involving physics-based, maneuver-based, and interaction-aware designs [3; 4]. Among them, the interaction-aware proposals have become widely appealing for their outstanding performance in handling the prediction issues [5; 6]. In the interaction-aware methods, the tracks of nearby vehicles, together with target agent states, are concurrently considered, which significantly enhances the ability of prediction. The performance improvements of these methods are due to feeding the vehicle-vehicle interaction information [7; 8]. In practice, the traffic rules are another critical concern for vehicles when traveling across intersection and play significant roles in reshaping trajectories. However, there are rare existing works that take the traffic-rule factors into account and furthermore consider the interaction of vehicle-traffic rules in trajectory prediction problems, which may lead to evaluation bias. In addition, the traffic rules and vehicle trajectory data are heterogeneous, where the first one is time-series data and the latter belongs to spatial-temporal sequences, thus inducing another key problem of how to integrate them through a united network. In reality, the trajectory anticipation task hinges on how to effectually extract representations from historical movements in complex scenarios. As in conventional traffic prediction, the spatial-temporal dependencies are emphasized to execute anticipation of the trajectory [9; 10; 11]. Recently, machine learning techniques have offered a promising strategy for integrating multi-modal features in virtue of their competence in data mining, wherein time-series-based approaches, such as the CNN and RNN, have been broadly adopted in the setting of trajectory prediction [12; 13; 14]. Nevertheless, these methods are not adequately suitable for non-Euclidean domains, especially for intersection scenes, where interactive operations frequently occur. Inspired by the topology structure, the graph convolutional network (GCN) is introduced to depict vehicle relationships, thus solving the problem of trajectory prediction [3; 15; 16]. These initial attempts regard the connection intensity as the function of the distance between nodes in the graph, which leads to a lack of growing learning ability. In fact, the interactive concentration is a dynamic variable and depends on the degree of impact, which cannot be expressed briefly by space distance. As shown in Figure 1, when \(T=1\) s, vehicle3 exerts more influence on vehicle1 than vehicle4 since vehicle3 and vehicle1 are in the same lane, despite the longer distance between vehicle1 and vehicle3 than that between vehicle1 and vehicle4. Nevertheless, as \(T=2\) s, vehicle1 approaches the intersection, while vehicle4 leaves the research region, in which case the traffic rules dominate. From the above appearance-based observations, it can be concluded that the interactive relationships cause unremitting changes over the time windows and need to be properly distinguished. Figure 1: Illustration of the dynamic interaction.