Deep-learning-based vehicle trajectory prediction: A review

Abstract

Vehicle trajectory prediction enables autonomous vehicles to better reason about fast-changing driving scenarios and thus perform well-informed decision-making tasks. Among different prediction approaches, deep learning-based (DL-based) methodologies stand out because of their capabilities to efficiently summarise historical data, infer nonlinear behavioural patterns from human driving data, and perform long-horizon prediction. This work reviews the DL-based methods that have shown promising results, organising them in terms of usage of the input data, separating the encodings of the target vehicle's historical data, surrounding vehicle's historical data, and road layout data. In particular, this paper explores the relationships between the scope of the prediction components and the input data formats, as well as the connections with other elements in the same prediction framework, including vehicle interaction and road scene mining. This information is crucial to understand complex architectural decisions and to provide guidance for the design of improved solutions. This work also compares the performance of the most successful prediction models, establishing that appropriate encodings of vehicle interactions and road scenes improve trajectory prediction accuracy, with the best performance achieved by attention mechanism and Transformer-based models. Finally, this work discusses future research directions, including considerations for real-time applications.