loading
Documents

Research.Publish.Connect.

Paper

Authors: Julian Bock 1 ; Philipp Nolte 2 and Lutz Eckstein 1

Affiliations: 1 Institute for Automotive Engineering (ika), RWTH Aachen University, Steinbachstr. 7, Aachen and Germany ; 2 RWTH Aachen University, Aachen and Germany

ISBN: 978-989-758-374-2

Keyword(s): Prediction, Vulnerable Road Users, Pedestrian, Deep Learning, Automated Driving, Intersections.

Abstract: Intersections with connected infrastructure and vehicle sensors allow observing vulnerable road users (VRU) longer and with less occlusion than from a moving vehicle. Furthermore, the connected sensors are providing continuous measurements of VRUs at the intersection. Thus, we propose a data-driven prediction model, which benefits of the continuous, local measurements. While most approaches in literature use the most probable path to predict road users, it does not represent the uncertainty in prediction and multiple maneuver options. We propose the use of Recurrent Neural Networks fed with measured trajectories and a variety of contextual information to output the prediction in a local occupancy grid map in polar coordinates. By using polar coordinates, a reliable movement model is learned as base model being insensitive against blind spots in the data. The model is further improved by considering input features containing information about the static and dynamic environment as well as local movement statistics. The model successfully predicts multiple movement options represented in a polar grid map. Besides, the model can continuously improve the prediction accuracy without re-training by updating local movement statistics. Finally, the trained model is providing reliable predictions if applied on a different intersection without data from this intersection. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.172.234.236

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Bock, J.; Nolte, P. and Eckstein, L. (2019). Continuously Improving Model of Road User Movement Patterns using Recurrent Neural Networks at Intersections with Connected Sensors.In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-374-2, pages 319-326. DOI: 10.5220/0007675603190326

@conference{vehits19,
author={Julian Bock. and Philipp Nolte. and Lutz Eckstein.},
title={Continuously Improving Model of Road User Movement Patterns using Recurrent Neural Networks at Intersections with Connected Sensors},
booktitle={Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2019},
pages={319-326},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007675603190326},
isbn={978-989-758-374-2},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Continuously Improving Model of Road User Movement Patterns using Recurrent Neural Networks at Intersections with Connected Sensors
SN - 978-989-758-374-2
AU - Bock, J.
AU - Nolte, P.
AU - Eckstein, L.
PY - 2019
SP - 319
EP - 326
DO - 10.5220/0007675603190326

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.