Ego-centric traffic behavior understanding through multi-level vehicle trajectory analysis
Donghao Xu, Xu He, Huijing Zhao, Jinshi Cui, Hongbin Zha, Franck Guillemard, Stephane Geronimi, François Aioun
Abstract
This study proposes a multi-level trajectory analysis method for modeling traffic behavior from an ego-centric view, where on-road vehicle trajectories are collected based on the authors' previous studies of an on-board system consisting of multiple 2D lidar sensors. From an input set of trajectories, a set of hot regions (topics) that trajectory points most frequently present are first discovered using a sticky HDP-HMM; then, the major paths of the trajectories' transitions across different hot regions are extracted by recursively mining frequent subsequences of topics; and finally, paths are modeled using a hierarchical hidden Markov model (HHMM), where the intra-path dynamics is represented using an HMM, in which each state corresponds to a hot region, while the inter-path transition is assumed to be Markovian. The model could be used for behavior prediction, i.e. whenever a vehicle is detected in a scene, predicting which route it will probably follow and how its trajectory will probably develop over time, which is essential to interpreting the potential risks for longer time horizons. Experiments are conducted using a large set of vehicle trajectories collected from motorways in Beijing, and promising results are presented.
BibTeX
@inproceedings{icra2017_egocentrictraffi,
title = {Ego-centric traffic behavior understanding through multi-level vehicle trajectory analysis},
author = {Donghao Xu and Xu He and Huijing Zhao and Jinshi Cui and Hongbin Zha and Franck Guillemard and Stephane Geronimi and François Aioun},
booktitle = {ICRA 2017},
year = {2017}
}