Intent-Aware Long-Term Prediction of Pedestrian Motion

Abstract

We present a method to predict long-term motion of pedestrians, modeling their behavior as jump-Markov processes with their goal a hidden variable. Assuming approximately rational behavior, and incorporating environmental constraints and biases, including time-varying ones imposed by traffic lights, we model intent as a policy in a Markov decision process framework. We infer pedestrian state using a Rao-Blackwellized filter, and intent by planning according to a stochastic policy, reflecting individual preferences in aiming at the same goal.

Overview

If you use this work in your research, please cite our paper:

• V. Karasev, A. Ayvaci, B. Heisele, and S. Soatto
  Intent-Aware Long-Term Prediction of Pedestrian Motion
  In ICRA, 2016.

  @inproceedings{karasevAHS16,
  author = {Karasev, V. and Ayvaci, A. and Heisele, B. and Soatto, S.},
  title = {Intent-Aware Long-Term Prediction of Pedestrian Motion},
  booktitle = {ICRA},
  year = {2016},
  month = {May}