# LIransformer: A Transformer-Based Framework for Task Offloading in Vehicular Edge Computing
## 2 Related Work
### Trajectory Prediction  
Trajectory prediction problems can be categorized into two types based on different data types, namely continuous trajectory prediction problems and discrete trajectory prediction problems. The continuous trajectory prediction problem is a regression problem. [PERSON] et al. [7] developed an LSTM model which can learn general human movement and predict their future trajectories. [PERSON] et al. [8] proposed a short-term real-time trajectory coordinate point prediction method based on a GRU (Gated Recurrent Unit) cyclic neural network. This method improves the accuracy of real-time forecasting by updating the model parameters in real time. [PERSON] et al. [9] discussed a new traffic network modeling algorithm based on the context of traffic intersections that maps vehicle trajectory nodes into a high-dimensional space vector, so that Bi-GRU can be used to bidirectionally model the trajectory matrix for the purpose of prediction. [PERSON] et al. [10] designed a two-step predictive framework solely based on personal location data. This framework aims to address the prediction of visits to new places and adjust prediction resolution to account for probable explorations of new locations. [PERSON] et al. [11] proposed a T-pattern tree for trajectory prediction. The tree is constructed using trajectory patterns that represent specific areas, and it can serve as a predictor for the next location of a new trajectory by identifying the best-matching path within the tree. [PERSON] et al. [12] put forward a new method named RTMatch to predict the future location of a moving object using the storage structure, RTPT and HT, which can be updated dynamically and provide dynamic analysis of trajectory pattern according to real-time information. [PERSON] et al. [13] presented a next-location prediction approach based on an RNN and self-attention mechanism to predict trajectory patterns based on a sequence of discrete nodes. [PERSON] et al. [14] proposed DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories. DeepMove effectively utilizes the periodicity nature to augment the RNN for mobility prediction. [PERSON] et al. [15] created a geographically temporally awareness hierarchical attention network (GT-HAN) to distinguish different user preferences. Recent research proves that the Transformer outperforms other deep learning methods in trajectory prediction. [PERSON] et al. [16] proposed LatentFormer, a transformer-based model able to predict future vehicle trajectories by leveraging a novel technique to model interactions among dynamic objects in the scene. Accounting for the interaction between vehicles, [PERSON] et al. [17] proposed two spatial attention mechanisms to help the model understand the surrounding environment better and thus improve its prediction accuracy. [PERSON] et al. [18] introduced the Spatio-Temporal grAph tRansformer (STAR) framework, a novel framework for spatio-temporal trajectory prediction based purely on a self-attention mechanism, with TGConv, a Transformer-based graph convolution mechanism. [PERSON] et al. [19] proposed a novel neural architecture, Transformer-XL, which enables learning dependency beyond a fixed length without disrupting temporal coherence. [PERSON] et al. [20] used a low-rank approximation method to approximate a self-attention mechanism, which maintains high performance while reducing the computational cost. [PERSON] et al. [21] introduced reversible residual layers that reduce the memory consumption of the model and give the model the ability to handle larger datasets. [PERSON] et al. [22] proposed the Spatial-Temporal Graph Attention Network (STGAT) for traffic flow forecasting. They demonstrated that STGAT can be generalized directly not only to graphs with an arbitrary structure, but also to completely unseen graphs. None of the existing deep-learning-based prediction methods consider the features in VEC. These deep learning methods need a large amount of storage and computational resources.