Goal-Oriented Transformer to Predict Context-Aware Trajectories in Urban Scenarios

Abstract

The accurate prediction of road user behaviour is of paramount importance for the design and implementation of effective trajectory prediction systems. Advances in this domain have recently been centred on incorporating the social interactions between agents in a scene through the use of RNNs. Transformers have become a very useful alternative to solve this problem by making use of positional information in a straightforward fashion. The proposed model leverages positional information together with underlying information of the scenario through goals in the digital map, in addition to the velocity and heading of the agent, to predict vehicle trajectories in a prediction horizon of up to 5 s. This approach allows the model to generate multimodal trajectories, considering different possible actions for each agent, being tested on a variety of urban scenarios, including intersections, and roundabouts, achieving state-of-the-art performance in terms of generalization capability, providing an alternative to more complex models.

Keywords

• trajectory prediction
• urban scenarios
• transformer
• intelligent transportation systems