Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features

Xiao Li; Guy Rosman; Igor Gilitschenski; Cristian Ioan Vasile; Jonathan A. DeCastro; Sertac Karaman; Daniela Rus

RA-L 202153 citations

Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features

Xiao Li, Guy Rosman, Igor Gilitschenski, Cristian Ioan Vasile, Jonathan A. DeCastro, Sertac Karaman, Daniela Rus

Abstract

In this work, we propose a novel approach for integrating rules into traffic agent trajectory prediction. Consideration of rules is important for understanding how people behave-yet, it cannot be assumed that rules are always followed. To address this challenge, we evaluate different approaches of integrating rules as inductive biases into deep learning-based prediction models. We propose a framework based on generative adversarial networks that uses tools from formal methods, namely signal temporal logic and syntax trees. This allows us to leverage information on rule obedience as features in neural networks and improves prediction accuracy without biasing towards lawful behavior. We evaluate our method on a real-world driving dataset and show improvement in performance over off-the-shelf predictors.

BibTeX

@inproceedings{ral2021_vehicletrajector,
  title = {Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features},
  author = {Xiao Li and Guy Rosman and Igor Gilitschenski and Cristian Ioan Vasile and Jonathan A. DeCastro and Sertac Karaman and Daniela Rus},
  booktitle = {RA-L 2021},
  year = {2021}
}