# GAN-Based Generation of Synthetic Data for Vehicle Driving Events
### Related Works  
Considering the success of GANs in generating high-quality artificial images, several proposals have focused on generating time series using GANs. Common to these approaches is the need to model data variation over time. One of the drawbacks of vanilla GANs in time series data generation is the instability of the discriminator during training, which can lead to the generator producing only a limited number of outputs. In the papers from the last five years reviewed as part of this research, a common approach to generating synthetic time series data is to use RNN networks within the generator and discriminator of the GAN network. However, vanilla RNN networks present vanishing gradient or exploding gradient problems. In order to overcome this drawback, in most cases variants of RNN, GRU [9; 15; 16; 17; 18], or LSTM [1; 2; 4; 7; 8; 19; 20; 21; 22; 23; 24; 25; 26; 27] are used. Other types of networks used in the generation of time series data include Wasserstein GANs [28; 29; 30], Convolutional Neural Networks [20; 24; 29; 31; 32; 33], Mixture Density Networks [19], and transformer networks [9; 18]. The transformer architecture is currently the state of the art in neural networks, and is a topic of great interest in the time series data generation field. In most proposals, the generated data have three or fewer dimensions [2; 4; 9; 16; 19; 20; 21; 22; 23; 24; 26; 29; 30; 31; 32; 33; 34]. However, several papers involved the generation of multidimensional data with more than three dimensions [1; 7; 8; 9; 15; 17; 18; 25; 27; 28]. Several authors have also demonstrated the effectiveness of their models in downstream tasks. Most of these generated data to improve the accuracy of prediction models as compared to results using only real data [2; 7; 9; 16; 17; 23; 24; 25; 32]. In contrast, other authors have used the generated data in clustering processes [21; 33]. Biomedical signals generation is the field in which synthetic time series data are most widely used. Medical signals are generated from _electrocapalpharms_ (EEGs), _electrocapalpharms_ (ECGs), _electrocarquigrams_ (EMG), _photoplethysmography_ (PPG), and _intensive care units_ (ICUs) [1; 9; 20; 22; 23; 24; 29; 31]. Other types of generated data include data network traffic flows [28], sensory data from personal activities [19], driving trajectories [21], IoT traffic [25], _internal measurement unit_ (IMU) data [7], electric vehicle battery parameters [8], _content delivery network_ (CDN) traffic [4], network and computer system data [2], _channel impulse responses_ (CIR) [26], astronomical light curves [32], power demand [32], highway cut-in behaviors [18], power consumption [15; 30; 33], solar generation [33], machine fault diagnosis [9], photovoltaic power [16], hydration monitoring [17], and physiological signals and markers [27]. Concerning the generation of vehicle driving data, important works include those of [PERSON] et al. [21], who generated data for the longitude and latitude components of driving trajectories; [PERSON] et al. [7], who generated data from an IMU (longitudinal acceleration, lateral acceleration, pitch, yaw, and roll) to classify the driving style of portions of trips as normal or aggressive; and [PERSON] et al. [18], who generated data on lane change scenarios on highways. The sequences generated in these works were of short duration (up to 12 s) and the number of dimensions used was small (less than or equal to 5). The technical challenge faced in this research is greater than that of the above works, as it relatesto driving event data using multidimensional data with fifteen features and generating sequences of 24 s in duration, resulting in a more robust model. Because of this, the data sequences generated in our work are more realistic, as they capture distributions of greater complexity created by the presence of a greater number of variables for a longer amount of time.