# Compression of Vehicle Trajectories with a Variational Autoencoder
## 1 Introduction
A new tactical behavior planner is proposed, motivated by how expert human drivers behave in intersections and is made up of a three-module architecture [11]. High-reliability lane change maneuver prediction is achieved by combining Support Vector Machine (SVM) and Artificial Neural Network (ANN) methods by [PERSON], [PERSON], and [PERSON] [12]. The SVM and ANN classifiers predict the feasibility and suitability to change lane under certain environmental conditions. Three different classifiers to predict lane changes are compared, and the best performance is the proposed combined model with 94% accuracy for non-merge behavior and 78% accuracy for merge behavior. [PERSON] et al. have examined vehicle movement and lane changing predictions using ANN and SVM classifiers. In their paper [13], they evaluate the performance of two kinds of ANNs predicting the lateral motion of the ego vehicle. Vehicle-to-vehicle communication systems can perform such prediction extensible to the surrounding vehicles. These two ANN architectures are evaluated in two datasets to achieve different results in different datasets with different variability. The authors use a Nonlinear Autoregressive Neural Network (NARNN), which is specially tuned to predict time series in dynamical models, which indicates when mapping between inputs and outputs is desired. In conclusion, the authors propose a baseline method to evaluate the lateral position prediction algorithms' performance. The NARNN specially indicated to predict dynamical systems is not better than the baseline method. However, the FFNN can reduce the mean absolute error on the predictedpositions about 23% and 30% up to 4 s in the University of Peking and University of Alcala datasets, respectively. For lane change detection, an SVM classifier is tested. The SVM can detect precisely a lane change 3 s before it happens [13]. Maneuver detection and short-term forecasting of road vehicles are essential parts of the autonomous cars' behavior planning algorithms. Several articles address the problem of classifying the maneuvers of vehicles moving in the ego vehicle's environment based on their trajectory data [10; 11; 12; 13]. In [14], an LSTM RNN classifier is presented, which can classify 3 s of vehicle trajectory data to three lateral maneuver classes with 86% precision without using any information about surrounding vehicles. For this specific task, other approaches and input compilations are compared and discussed. On the other hand, a completely unified framework for surrounding vehicle maneuver recognition and motion prediction is presented with a greater generality by [PERSON] et al. [15]. Their framework outperforms an interacting multiple model-based trajectory prediction baselines and runs in real-time at about six frames per second. Trajectory prediction has an inherent uncertainty because of the surrounding vehicles, and it can be handled by multi-modal prediction. In [16], this problem is addressed even just in [10], but a CNN based approach is presented. Their approach first generates a raster image encoding each vehicle actor's context and uses a CNN model to output several possible trajectories and their probabilities. These operations are computationally intensive and require computational capabilities for real-time traffic analysis. On the other hand, the data transferred by V2X are sensitive and need secure communication, which places additional overhead on the computational requirements and hence hardens the real-time application [17; 18]. It is advisable to compress the trajectory data in such a way that the compressed data are at least one order of magnitude smaller and capable of reconstructing the real trajectory with relatively high accuracy. At the same time, carrying useful information about the latent properties beyond the data distribution.