Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation

Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanc...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2020-11, Vol.21 (11), p.4773-4783
Main Authors: Fang, Zhijie, Lopez, Antonio M.
Format: Article
Language:English
Subjects:
ADAS
Advanced driver assistance systems
Artificial neural networks
Autonomous driving
computer vision
cyclists intention recognition
Datasets
Legged locomotion
Maneuvers
Monocular vision
Pedestrians
pedestrians intention recognition
Pose estimation
Recognition
Roads
Skeleton
Task analysis
Two dimensional displays
Videos
Walking
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Summary:Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly ( e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2019.2946642