AIMC Topic: Pedestrians

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Exploring the social life of urban spaces through AI.

Proceedings of the National Academy of Sciences of the United States of America Jul 24, 2025
We analyze changes in pedestrian behavior over a 30-y period in four urban public spaces located in New York, Boston, and Philadelphia. Building on William Whyte's observational work, which involved manual video analysis of pedestrian behaviors, we e...
Philadelphia Boston Walking Pedestrians Humans Artificial Intelligence Cities Urban Population
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Enhanced pedestrian trajectory prediction via overlapping field-of-view domains and integrated Kolmogorov-Arnold networks.

PloS one Jun 9, 2025
Accurate pedestrian trajectory prediction is crucial for applications such as autonomous driving and crowd surveillance. This paper proposes the OV-SKTGCNN model, an enhancement to the Social-STGCNN model, aimed at addressing its low prediction accur...
Walking Pedestrians Neural Networks, Computer Algorithms Humans Models, Theoretical
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High-order diversity feature learning for pedestrian attribute recognition.

Neural networks : the official journal of the International Neural Network Society Apr 11, 2025
Pedestrian attribute recognition (PAR) involves accurately identifying multiple attributes present in pedestrian images. There are two main approaches for PAR: part-based method and attention-based method. The former relies on existing segmentation o...
Humans Pedestrians Attention Pattern Recognition, Automated Neural Networks, Computer Machine Learning Algorithms
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Modeling crash avoidance behaviors in vehicle-pedestrian near-miss scenarios: Curvilinear time-to-collision and Mamba-driven deep reinforcement learning.

Accident; analysis and prevention Mar 4, 2025
Interactions between vehicle-pedestrian at intersections often lead to safety-critical situations. This study aims to model the crash avoidance behaviors of vehicles during interactions with pedestrians in near-miss scenarios, contributing to the dev...
Deep Learning Time Factors Models, Theoretical Algorithms Computer Simulation Safety Reinforcement Machine Learning Avoidance Learning Environment Design Pedestrians Accidents, Traffic Humans Unmanned Aerial Devices
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Simulation of human-vehicle interaction at right-turn unsignalized intersections: A game-theoretic deep maximum entropy inverse reinforcement learning method.

Accident; analysis and prevention Feb 19, 2025
The safety of pedestrians in urban transportation systems has emerged as a significant research topic. As a vulnerable group within this transportation framework, pedestrians encounter heightened safety risks in complex urban road environments. Prote...
Markov Chains Entropy Reinforcement, Psychology Game Theory Pedestrians Accidents, Traffic Safety Environment Design Walking Humans Algorithms Computer Simulation Deep Learning
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Pedestrian POSE estimation using multi-branched deep learning pose net.

PloS one Jan 24, 2025
In human activity-recognition scenarios, including head and entire body pose and orientations, recognizing the pose and direction of a pedestrian is considered a complex problem. A person may be traveling in one sideway while focusing his attention o...
Pedestrians Deep Learning Posture Humans Algorithms
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A Semantic-Aware Attention and Visual Shielding Network for Cloth-Changing Person Re-Identification.

IEEE transactions on neural networks and learning systems Jan 7, 2025
Cloth-changing person re-identification (ReID) is a newly emerging research topic that aims to retrieve pedestrians whose clothes are changed. Since the human appearance with different clothes exhibits large variations, it is very difficult for exist...
Humans Biometric Identification Neural Networks, Computer Pedestrians Algorithms Clothing Attention Semantics
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Investigating streetscape environmental characteristics associated with road traffic crashes using street view imagery and computer vision.

Accident; analysis and prevention Nov 23, 2024
Examining the relationship between streetscape features and road traffic crashes is vital for enhancing roadway safety. Traditional field surveys are often inefficient and lack comprehensive spatial coverage. Leveraging street view images (SVIs) and ...
Accidents, Traffic Image Processing, Computer-Assisted Safety Environment Design Humans Built Environment Pedestrians Deep Learning
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Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach.

Accident; analysis and prevention Nov 19, 2024
Jaywalking, as a hazardous crossing behavior, leaves little time for drivers to anticipate and respond promptly, resulting in high crossing risks. The prevalence of Autonomous Vehicle (AV) technologies has offered new solutions for mitigating jaywalk...
Automobiles Humans Reinforcement, Psychology Walking Accidents, Traffic Deep Learning Risk Assessment Pedestrians Automobile Driving Decision Making Automation
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Predicting pedestrian-vehicle interaction severity at unsignalized intersections.

Traffic injury prevention Oct 15, 2024
OBJECTIVES: This study aims to develop and validate a novel deep-learning model that predicts the severity of pedestrian-vehicle interactions at unsignalized intersections, distinctively integrating Transformer-based models with Multilayer Perceptron...
Environment Design Pedestrians Accidents, Traffic Safety Humans Deep Learning
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