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

Abstract

The problem of creating a fully autonomous vehicle is one of the most urgent in the field of artificial intelligence. Many companies claim to sell such cars in certain working conditions. The task of interacting with other road users is to detect them, determine their physical properties, and predict their future states. The result of this prediction is the trajectory of road users’ movement for a given period of time in the near future. Based on such trajectories, the planning system determines the behavior of an autonomous-driving vehicle. This paper demonstrates a multi-agent method for determining the trajectories of road users, by means of a road map of the surrounding area, working with the use of convolutional neural networks. In addition, the input of the neural network gets an agent state vector containing additional information about the object. A number of experiments are conducted for the selected neural architecture in order to attract its modifications to the prediction result. The results are estimated using metrics showing the spatial deviation of the predicted trajectory. The method is trained using the nuscenes test dataset obtained from lgsvl-simulator.