# GAN-Based Generation of Synthetic Data for Vehicle Driving Events  
[PERSON]  
Departamento de Informatica y Ciencias de la Computacion, Escuela Politecnica Nacional, Edificio de Sistemas, Quito 170525, Ecuador; [PERSON]SS] (D.T.-U. ); [PERSON]@epn.edu.e (S.-G.); [PERSON]@epn.edu.e (M.H.-A.) [PERSON]  
Departamento de Informatica y Ciencias de la Computacion, Escuela Politecnica Nacional, Edificio de Sistemas, Quito 170525, Ecuador; [PERSON]SS] (D.T.-U. ); [PERSON]@epn.edu.e (S.-G.); [PERSON]@epn.edu.e (M.H.-A.) [PERSON]  
Correspondence: [PERSON]L_ADDRESS]###### Abstract  
Developing solutions to reduce traffic accidents requires experimentation and much data. However, due to confidentiality issues, not all datasets used in previous research are publicly available, and those that are available may be insufficient for research. Building datasets with real data is costly. Given this reality, this paper proposes a procedure to generate synthetic data sequences of driving events using the Time series GAN (TimeGAN) and Real-world time series (RTSGAN) frameworks. First, a 15-feature driving event dataset is constructed with real data, which forms the basis for generating datasets using the two mentioned frameworks. The generated datasets are evaluated using the qualitative metrics PCA and T-SNE, as well as the discriminative and predictive score quantitative metrics defined in TimeGAN. The generated synthetic data are then used in an unsupervised algorithm to identify clusters representing vehicle crash risk levels. Next, the generated data are used in a supervised classification algorithm to predict risk level categories. Comparison results between the data generated by TimeGAN and RTSGAN show that the data generated by RTSGAN achieve better scores than the the data generated with TimeGAN. On the other hand, we demonstrate that the use of datasets trained with synthetic data to train a supervised classification model for predicting the level of accident risk can obtain accuracy comparable to that of models that use datasets with only real data in their training, proving the usefulness of the generated data. s +
Footnote †: journal: Medical  
can be used to fill out an initial dataset with driving data gathered from heterogeneous sources. Qualitative and quantitative methods are employed to assess the synthetic data generation results and establish their usefulness. The main objective of this research is to generate synthetic data using GAN-based frameworks for synthetic time series data and subsequently use the data in tasks to train prediction models for classifying the risk level of certain ways of driving vehicles. The data generated are obtained from a dataset with real information, which is made available to the general public in this work. A dataset with real information provides an answer to the lack of adequate datasets for study. The set of data generated in this research is used in processes to predict the level of risk of accidents in vehicles. Although publicly available datasets exist, they do not incorporate data from vehicle sensors, drivers, and the environment into the same dataset. In addition, these datasets correspond to countries with realities that are different from that of Ecuador in aspects such as road geometry, road signs, and driving culture, meaning that they are not immediately applicable to local research. Although the code for the TimeGAN and RTSGAN frameworks is publicly available, the datasets on which they are based are not related to vehicle driving events, meaning that the distributions in the data respond to other realities; as a result, the hyperparameters published in the papers presenting these frameworks are not directly applicable to vehicle driving event-related datasets. The scientific contributions of this article are as follows:  
1. An initial multidimensional dataset of driving events is constructed with real data from heterogeneous sources, then used to generate synthetic data. 2.