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Cited by 36Advanced Neural Network ApplicationsAutonomous Vehicle Technology and SafetyDomain Adaptation and Few-Shot Learning

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

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Mordan, Taylor, Cord, Matthieu, Perez, Patrick, Alahi, Alexandre·2022·IEEE Transactions on Intelligent Transportation Systems
DOI 10.1109/tits.2021.3107587
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Abstract

Pedestrians are arguably one of the most safety-critical road users to consider for autonomous vehicles in urban areas. In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes from a single image. These encompass visual appearance and behavior, and also include the forecasting of road crossing, which is a main safety concern. For this, we introduce a Multi-Task Learning (MTL) model relying on a composite field framework, which achieves both goals in an efficient way. Each field spatially locates pedestrian instances and aggregates attribute predictions over them. This formulation naturally leverages spatial context, making it well suited to low resolution scenarios such as autonomous driving. By increasing the number of attributes jointly learned, we highlight an issue related to the scales of gradients, which arises in MTL with numerous tasks. We solve it by normalizing the gradients coming from different objective functions when they join at the fork in the network architecture during the backward pass, referred to as fork-normalization. Experimental validation is performed on JAAD, a dataset providing numerous attributes for pedestrian analysis from autonomous vehicles, and shows competitive detection and attribute recognition results, as well as a more stable MTL training.

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  • AVs can enable safe interactions with pedestrians in urban contexts by making decisions that take into consideration the presence and characteristics of pedestrians.

    “AVs can enable safe interactions with pedestrians in urban contexts by making decisions that take into consideration the presence and characteristics of pedestrians [52].”

    From Assessing the Role of Autonomous Vehicles in Urban Areas: A Systematic Review of Literature (2024) · cites this paper as [52]

  • Planning principles for mobility services based on AVs, such as less flexible travel motivations, could have a direct effect on passengers.

    “Additionally, the planning principles for mobility services based on AVs such as less flexible travel motivations could have a direct effect on passengers [52].”

    From Assessing the Role of Autonomous Vehicles in Urban Areas: A Systematic Review of Literature (2024) · cites this paper as [52]

    The Introduction (p. 1) concerns detecting pedestrian attributes, and the Conclusions (p. 10) discuss how an autonomous vehicle's behavior can affect pedestrians' decisions. Searches for less flexible travel motivations, mobility-service planning, and effects on passengers found no passage addressing the cited claim. (LLM, conf=0.76)

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Authors
Mordan, T., Cord, M., Perez, P., & Alahi, A.
Affiliations
  • École Polytechnique Fédérale de Lausanne (CH)
  • Sorbonne Université (FR)
  • Valeo.ai, Paris, France
ORCID
Venue
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022-08
Volume
23
Issue
8
Pages
11823-11835
ISSN
  • 1524-9050 (ISSN-L)
  • 1558-0016
DOI
10.1109/tits.2021.3107587
OpenAlex
W3111881137
Cited by
36
References
4
Citations by year
2026: 2 · 2025: 8 · 2024: 12 · 2023: 6 · 2022: 4 · 2021: 4
FWCI
2.34
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
  • arXiv (Cornell University)
Funders
Valeo
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
pedestrian detection, pedestrian attribute recognition, autonomous vehicles, multi-task learning, gradient scaling, spatial context, low-resolution images, JAAD dataset

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