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Found 10 papers, 3 papers with code
A Framework for Pedestrian Sub-classification and Arrival Time Prediction at Signalized Intersection Using Preprocessed Lidar Data
The mortality rate for pedestrians using wheelchairs was 36% higher than the overall population pedestrian mortality rate.
Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach
In many spatial trajectory-based applications, it is necessary to map raw trajectory data points onto road networks in digital maps, which is commonly referred to as a map-matching process.
Analyzing vehicle pedestrian interactions combining data cube structure and predictive collision risk estimation model
In particular, this study focuses on the latter by introducing a new analytical framework for a crosswalk safety assessment with behaviors of vehicle/pedestrian and environmental features.
Automated Object Behavioral Feature Extraction for Potential Risk Analysis based on Video Sensor
In this paper, we propose an automated and simpler system for effectively extracting object behavioral features from video sensors deployed on the road.
A novel method of predictive collision risk area estimation for proactive pedestrian accident prevention system in urban surveillance infrastructure
Road traffic accidents, especially vehicle pedestrian collisions in crosswalk, globally pose a severe threat to human lives and have become a leading cause of premature deaths.
Vision based Pedestrian Potential Risk Analysis based on Automated Behavior Feature Extraction for Smart and Safe City
Despite recent advances in vehicle safety technologies, road traffic accidents still pose a severe threat to human lives and have become a leading cause of premature deaths.
A Real-Time Predictive Pedestrian Collision Warning Service for Cooperative Intelligent Transportation Systems Using 3D Pose Estimation
To achieve the goal, pedestrian orientation recognition and prediction of pedestrian's crossing or not-crossing intention play a central role.
TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning
A generative model for urban vehicle trajectories can better generalize from training data by learning the underlying distribution of the training data and, thus, produce synthetic vehicle trajectories similar to real vehicle trajectories with limited observations.
Traffic Data Imputation using Deep Convolutional Neural Networks
We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information.
Attention-based Recurrent Neural Network for Urban Vehicle Trajectory Prediction
With the increasing deployment of diverse positioning devices and location-based services, a huge amount of spatial and temporal information has been collected and accumulated as trajectory data.
