Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks

  • In this work, we use Recurrent Neural Networks (RNNs) in form of Gated Recurrent Unit (GRU) networks to forecast trajectories of vulnerable road users (VRUs), such as pedestrians and cyclists, in road traffic utilizing the past trajectory and 3D poses as input. The 3D poses represent the postures and movements of limbs and torso and contain early indicators for the transition between motion types, e.g. wait, start, move, and stop. VRUs often only become visible from the perspective of an approaching vehicle shortly before dangerous situations occur. Therefore, a network architecture is required which is able to forecast trajectories after short time periods and is able to improve the forecasts in case of longer observations. This motivates us to use GRU networks, which are able to use time series of varying duration as inputs, and to investigate the effects of different observation periods on the forecasting results. Our approach is able to make reasonable forecasts even for short observation periods. The use of poses improves the forecasting accuracy, especially for short observation periods compared to a solely head trajectory based approach. Different motion types benefit to different extent from the use of poses and longer observation periods.
Metadaten
Author:Viktor Kreß, Stefan Zernetsch, Konrad Doll, Bernhard Sick
DOI:https://doi.org/https://doi.org/10.1007/978-3-030-68763-2_5
ISBN:978-3-030-68763-2
Parent Title (English):ICPR 2021: Pattern Recognition. ICPR International Workshops and Challenges
Publisher:Springer International Publishing
Place of publication:Cham
Document Type:Conference Proceeding
Language:English
Year of Completion:2020
Release Date:2021/02/24
Tag:3D body pose; 3D human trajectory forecast; Vulnerable road users
GND Keyword:Fahrerassistenzsystem; Fußgänger; Radfahrer
Page Number:15
First Page:57
Last Page:71
Urheberrecht:1
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Intelligent Systems / Intelligent Mobility
Intelligent Systems / Intelligent Sensors and Signals
Licence (German):Keine Lizenz - es gilt das deutsche Urheberrecht
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