# Deep Learning-Based Multimodal Trajectory Prediction with Traffic Light
## 5 Conclusions  
Trajectory prediction is an essential component of safe driving by autonomous agents. Attempts to model the complex interactions of multiple factors in the real world using deep learning techniques are ongoing, and researchers are striving to go beyond simply utilizing the location information of a single agent to reflect interactions with dynamic objects or to efficiently reflect the effects of road topography. In this paper, we study the prediction of multiple trajectories of multiple agents in an urban intersection context through a generative model. Building on existing research that models the temporal dependence and interdependence between agents on each agent's trajectory, we reflect the influence of dynamic agent states such as speed, direction, traffic light status, and scene context information. We utilized ADE and FDE, metrics used in deep learning models for multiple trajectory generation, to compare the results over time for each prediction, and compared the results with and without agent state values. We believe that this paper is meaningful in that it is a model that performs trajectory prediction in urban areas by considering interactions in various real-world situations. However, predicting multiple trajectories of multiple agents is considered a difficult task, and the results obtained in this study leave much to be desired in terms of accuracy. Research is needed to improve this accuracy, consider the temporal impact of trajectory generation, and better reflect traffic light conditions. Furthermore, future research could consider models that reflect factors such as lanes and road markings. Considering the characteristics of autonomous vehicles that require cognition and planning in real time, it is necessary to continue research and development to efficiently model complex environmental factors. It is also believed that changing the encoder, for example, with a Transformer or GCN, which is a modern model for time series data, could lead to an improvement in performance and time complexity [32]. Obtaining contextual information from sensors is also an area where autonomous communication technologies need to be integrated, so technologies such as edge caching may be able to improve the accuracy of trajectory prediction by reducing the latency involved [33]. Therefore, a combination of approaches from different perspectives is required to improve accuracy and speed. Written with joint contributions from the authors. Conceptualization, S.L. and H.P. ; methodology, S.L. and H.P. ; software, S.L. and H.P. ; validation, S.Y. and Y.Y. ; formal analysis, S.L. ; investigation, S.L. and H.P. ; resources, S.L. and H.P. ; writing--original draft preparation, S.L. ; writing--review and editing, H.P., S.Y. and Y.Y. ; visualization, H.P. and S.L. ; supervision, I.-Y.M. ; All authors have read and agreed to the published version of the manuscript. This research was supported by the Basic Research Program through the National Research Foundation of Korea (NRF) and funded by the Ministry of Education (No. 2021R1 IIA3057800), and the results were supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-004). Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Publicly available datasets, Wateloo Multi-Agent Traffic Dataset were analyzed in this study.