Algorithm for Predicting the Trajectory of Road Users to Automate Control of an Autonomous Vehicle

Abstract

One of the problems faced by developers of artificial intelligence algorithms when creating car control systems is that the actions of other road users are difficult to predict and have a large variability. Even if we assume that all actions comply with traffic rules and participants do not make mistakes, that is, to bring the actual environment closer to the ideal, the task of automating vehicle control still contains many difficulties. This paper describes what difficulties exist in the field of predicting the trajectory of objects, shows concepts that will help in solving this problem, and also describes a particular method of forecasting, which allows you to make a forecast for cars moving along traffic lanes. The main forecasting stages and the results of testing the method collected by using a ready-made data set are given. The results presented in the form of a set of metrics, are compared with another algorithm for predicting trajectories. As a result, the advantages and disadvantages of the created solution were identified.

Multi-agent Approach to Predict the Trajectory of Road Infrastructure Agents Using a Convolutional Neural Network

“…Most often, they are used for the movement of the unmanned car itself, but since these maps repeat public roads, their use allows to predict the behavior of agents more accurately. In [13], the prediction of cars behavior was based on the use of the map of the surrounding area.…”