# Compression of Vehicle Trajectories with a Variational Autoencoder
## 1 Introduction  
The general approach for modeling autonomous vehicles contains hierarchical three-layered modeling, i.e., the perception layer, a planning layer, and a trajectory control layer. The perception layer preprocesses data from multiple sensors (lidar, radar, video camera, GPS, etc.) to fuse them into a reliable perception of the surrounding environment and traffic situation. The trajectory control layer drives the actuators and gives commands based on the second planning layer. This contains three sublayers: route planning, behavioral decision-making, and path planning [1]. The route and path planning layer generates a global route and feasible local trajectories. The behavioral decision-making layer provides safe and feasible driving actions on the strategic level, such as \"left lane change, follow, etc.\". Self-driving vehicles' decision-making system should foresee the near future to predict the surrounding vehicles' future driving behavior [2]. Without the prediction function, emergency incidents may happen, such as the collision between MIT's \"Talos\" AVand Cornell's \"Skynet\" AV during the 2007 urban challenge, which was held by the Defense Advanced Research Projects Agency (DAPRA) [3]. Autonomous vehicles must navigate safely and efficiently in a complex vehicle traffic system, either by individual or cooperative decisions [4]. To do this, they need to make decisions continuously about what steps to take in a given traffic situation, such as when to start an overtaking or lane change maneuver. A necessary condition for decision-making is that the autonomous vehicle could interpret and consider the movement of surrounding vehicles' behavior [5]. The behavioral prediction for autonomous driving is challenging and surrounded by great attention. [PERSON], [PERSON], and [PERSON] present a hierarchical dynamic Bayesian network utilized for predicting behavioral patterns [6]. Gaussian process regressions are also used for trajectory pattern classification and prediction by [PERSON] and [PERSON] [7]. The Gaussian mixture model for trajectory prediction is also a subject of research. [PERSON] et al. [8] present a solution which can predict the future trajectory by learning patterns in trajectories, to infer a joint probability distribution as a motion model. The future path is predicted by calculating the probability for the motion, conditioned on the current observed trajectory. Their work's novelty is that they provide a distribution over the future trajectories. Hence, the evaluation of the statistical properties can predict a specific scenario. [PERSON] et al. present a study of location prediction applied to trajectories obtained from sensors placed on road networks [9]. A variety of Recurrent Neural Networks (RNN) is applied using a different combination of features to measure the features' impact on the prediction task. The authors show the underlined road network's use to estimate a finer granularity trajectory definition and obtain better models in terms of accuracy and error of distance. A novel approach presents a Long-Short Term Memory (LSTM) model for motion prediction of surrounding vehicles on freeways, aware of all the surrounding cars [10]. This model's output is not a single motion trajectory but a multi-modal distribution over future motion. A trajectory encoder LSTM encodes the vehicle's track histories and relative positions being predicted and its adjacent vehicles in a context vector. The context vector is appended with maneuver encodings of the lateral and longitudinal maneuver classes. The decoder LSTM generates maneuver specific future distributions of vehicle positions at each time step, and the maneuver classification branch assigns maneuver probabilities. [PERSON] et al. claim that, in a system consisting of three planners (global path, behavioral, and local path), the behavior planner is the limitation of a successful oath planning solution.