Integrated Graphical Representation of Highway Scenarios to Improve Trajectory Prediction of Surrounding Vehicles

Abstract

Varying numbers and types of vehicles, various road structures and traffic rules bring difficulties to an autonomous vehicle driving in highway traffic scenarios. It is important to simultaneously consider all these elements in an integrated framework when predicting the future trajectories of surrounding vehicles. This paper presents a unified graphical representation method for dynamic traffic scenarios based on integrating not only the constraints from vehicles but also the collision risk implied behind road structures and traffic rules. Different from previous studies which ignores road structures and traffic rules or separately represent them in a qualitative way, this method can make better use of the influences of these environment elements in a quantitative way to improve trajectory prediction of surrounding vehicles in highway scenarios. Specifically, this method first assesses the permissibility and passability of different types of roads and vehicles, and then defines their collision risk level based on traffic rules like speed limit or priority signs. Secondly, the bird-view scenarios are rasterized and rendered as color-scaled feature maps based on this risk level. Finally, multiple feature maps with various spatial and temporal perspective field are sequentially fed to a convolutional long short-term memory (LSTM) network to predict trajectories for surrounding vehicles. We implement the model on NGSIM dataset and achieve satisfying prediction accuracy, for example, the error over 5s time prediction horizon can be reduced to less than 2.78m in longitudinal and 0.26m in lateral direction.