## 2 Related Work  
With the continued information perception, autonomous vehicles are required to speculate road shares' movements and then conduct safe path planning and decision making in real time to realize a collision-free path. However, in highly complex traffic architectures, especially for the intersection scenes, implementing trajectory anticipation is not a trivial task since there are several challenges. Firstly, in contrast to intention prediction, the trajectory anticipation problem requires generating more exhaustive information over a duration of time, which increases the demand for model performance. Secondly, there are assorted traffic participants and the degree of mutual influence varies, resulting in the difficulties of how to distinguish the effective impact or not and then how to properly evaluate their interactions. Additionally, traffic rules can reshape vehicles' driving behavior and should be taken into account, which also adds to the complexity of prediction. A number of works have been devoted to partly addressing these trick issues, and such approaches can be broadly partitioned according to their input representations. The first thread is mainly regarded as the conventional method with the target vehicle information as the input, including vehicle positions, speed, operation elements, driving styles and so on [17; 18]. To extract the contextual attributes of road networks, a Bayesianmethod combining uncertain observations was proposed for achieving the vehicle maneuvering intention evaluation [19]. [PERSON] and [PERSON] introduced the Hidden Markov Models into a trajectory prediction framework and showed that the learning accuracy depends on both the size of the data and the data quality, which transformed the prediction task to the classifying problem [20]. Based on the autoregressive moving average model, [PERSON] et al. developed a two-level quantified adaptive Kalman filter algorithm to predict vehicle location and velocity and then broadcasted the vehicle states to each other via the road unit [21]. These schemes only leverage the target vehicle features and ignore the movements of other nearby agents, which can lead to errors in the process of trajectory evaluation in crowded driving situations. Inspired by the social interaction, recently the tracks of surrounding vehicles have been incorporated into studies, and a number of interaction-aware models have been developed based on the fact that the vehicles in the traffic environments are interplaying [22]. Here, the machine learning methods are widely adopted and demonstrate preferable performance in dealing with the trajectory prediction problem [23; 24]. [PERSON] et al. employed an LSTM model to classify the observed vehicles maneuvers considering the neighboring features, which outperforms the baselines of conditional probability table and multilayer perceptron models [25]. Similarly, [PERSON] et al. proposed an LSTM model for vehicle motion prediction on freeways by utilizing an NGSIM dataset [26]. To improve the estimation accuracy, [PERSON] et al. introduced a Multi-Agent-Tensor Fusion network and evaluated its performance in both highway and pedestrian crowd scenes, incorporating scene context constraints and vehicle interactions [27]. Nevertheless, the above proposals neglect the spatial features in conducting the trajectory evaluation. To tackle the drawback, [PERSON] et al. designed an improved LSTM vehicle motion forecasting scheme embedded with multi-head attention aiming to realize the interactions and uncertainty estimation [28]. Using the clustering method and LSTM model, [PERSON] et al. integrated the intention prediction and trajectory prediction tasks, where the statistical law of trajectory was used to provide prior knowledge for latter anticipation [29]. However, as the number of stacked layers increases, the performance of the RNN-based prediction models is gently ameliorated while the computation sources exponentially grow, which is obviously not suitable for the real-time requirements of autonomous vehicles [30].