AIMC Topic: Pedestrians

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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...
Models, Theoretical Walking Pedestrians Humans Neural Networks, Computer 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...
Reinforcement Machine Learning Avoidance Learning Unmanned Aerial Devices Pedestrians Accidents, Traffic Safety Algorithms Computer Simulation Deep Learning Time Factors Models, Theoretical Environment Design Humans
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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...
Humans Algorithms Computer Simulation Pedestrians Accidents, Traffic Safety Deep Learning Environment Design Walking Reinforcement, Psychology Game Theory Markov Chains Entropy
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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...
Humans Algorithms Deep Learning Posture Pedestrians
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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...
Biometric Identification Clothing Humans Neural Networks, Computer Algorithms Pedestrians 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 ...
Humans Deep Learning Image Processing, Computer-Assisted Accidents, Traffic Environment Design Safety Pedestrians Built Environment
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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...
Humans Risk Assessment Deep Learning Decision Making Automobiles Walking Automobile Driving Pedestrians Automation Accidents, Traffic Reinforcement, Psychology
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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...
Pedestrians Accidents, Traffic Safety Deep Learning Environment Design Humans
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Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning.

Accident; analysis and prevention Jul 1, 2024
Examining the relationship between streetscape features and road traffic accidents is pivotal for enhancing roadway safety. While previous studies have primarily focused on the influence of street design characteristics, sociodemographic features, an...
Deep Learning Environment Design Pedestrians Machine Learning Algorithms Humans Accidents, Traffic Safety Built Environment
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Real-Time Tracking Data and Machine Learning Approaches for Mapping Pedestrian Walking Behavior: A Case Study at the University of Moratuwa.

Sensors (Basel, Switzerland) Jun 13, 2024
The growing urban population and traffic congestion underline the importance of building pedestrian-friendly environments to encourage walking as a preferred mode of transportation. However, a major challenge remains, which is the absence of such ped...
Cluster Analysis Algorithms Sri Lanka Humans Walking Pedestrians Geographic Information Systems Machine Learning Universities Cell Phone
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