Proceedings ArticleDOI
Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior
Amir Rasouli,Iuliia Kotseruba,John K. Tsotsos +2 more
- pp 206-213
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TLDR
A novel dataset is introduced which in addition to providing the bounding box information for pedestrian detection, also includes the behavioral and contextual annotations for the scenes, which allows combining visual and semantic information for better understanding of pedestrians' intentions in various traffic scenarios.Abstract:
Designing autonomous vehicles suitable for urban environments remains an unresolved problem. One of the major dilemmas faced by autonomous cars is how to understand the intention of other road users and communicate with them. The existing datasets do not provide the necessary means for such higher level analysis of traffic scenes. With this in mind, we introduce a novel dataset which in addition to providing the bounding box information for pedestrian detection, also includes the behavioral and contextual annotations for the scenes. This allows combining visual and semantic information for better understanding of pedestrians' intentions in various traffic scenarios. We establish baseline approaches for analyzing the data and show that combining visual and contextual information can improve prediction of pedestrian intention at the point of crossing by at least 20%.read more
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Posted Content
Network Generalization Prediction for Safety Critical Tasks in Novel Operating Domains.
TL;DR: In this paper, the authors identify the context subspace for a pretrained Faster RCNN network performing pedestrian detection on the Berkeley Deep Drive (BDD) dataset and demonstrate that the Context Subspace from the BDD dataset is informative for completely unseen datasets, such as JAAD and Cityscapes.
Journal ArticleDOI
Pedestrian Crossing Intention Forecasting at Unsignalized Intersections Using Naturalistic Trajectories
Esteban Moreno,Patrick Eoghan Denny,Enda Peter Ward,Jonathan Horgan,Ciaran Eising,Edward Jones,Martin Glavin,Ashkan Parsi,Darragh Mullins,Brian Michael Thomas Deegan +9 more
TL;DR: In this paper , a model that predicts pedestrian crossing behavior at different locations around an urban intersection is proposed, which not only provides a classification label (e.g., crossing, not-crossing), but also a quantitative confidence level (i.e., probability).
Journal ArticleDOI
ST CrossingPose: A Spatial-Temporal Graph Convolutional Network for Skeleton-Based Pedestrian Crossing Intention Prediction
TL;DR: Wang et al. as mentioned in this paper proposed a spatial-temporal graph convolutional network (ST CrossingPose) to predict pedestrian crossing intention based on skeleton data, which can learn both spatial and temporal patterns from skeleton data.
Journal ArticleDOI
U19-Net: a deep learning approach for obstacle detection in self-driving cars
TL;DR: U19-Net as mentioned in this paper proposes an encoder-decoder deep model that explores the deep layers of a VGG19 model as encoder following a symmetrical approach with an U-Net decoder designed for pixel-wise classifications.
Book ChapterDOI
Road Scene Risk Perception for Intelligent Vehicles Using End-to-End Affordance Learning and Visual Reasoning
TL;DR: A new perspective on scene risk perception is proposed by modeling end-to-end road scene affordance using a weakly supervised classifier and results show that the proposed model is able to correctly classify three different levels of risk.
References
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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Navneet Dalal,Bill Triggs +1 more
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Journal ArticleDOI
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