AIMC Topic: Pedestrians
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Forecasting Pedestrian Movements Using Recurrent Neural Networks: An Application of Crowd Monitoring Data.
Sensors (Basel, Switzerland)
Jan 18, 2019
Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting tech...
Pedestrian's risk-based negotiation model for self-driving vehicles to get the right of way.
Accident; analysis and prevention
Jan 17, 2019
Negotiations among drivers and pedestrians are common on roads, but it is still challenging for a self-driving vehicle to negotiate for its right of way with other human road users, especially pedestrians. Currently, the self-driving vehicles are pro...
Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning.
IEEE transactions on cybernetics
Nov 20, 2018
Pedestrian regulation can prevent crowd accidents and improve crowd safety in densely populated areas. Recent studies use mobile robots to regulate pedestrian flows for desired collective motion through the effect of passive human-robot interaction (...
The Moral Machine experiment.
Nature
Oct 24, 2018
With the rapid development of artificial intelligence have come concerns about how machines will make moral decisions, and the major challenge of quantifying societal expectations about the ethical principles that should guide machine behaviour. To a...
On-Board Detection of Pedestrian Intentions.
Sensors (Basel, Switzerland)
Sep 23, 2017
Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of re...
A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data.
Sensors (Basel, Switzerland)
Dec 23, 2016
This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a ...
Constrained Metric Learning by Permutation Inducing Isometries.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Nov 19, 2015
The choice of metric critically affects the performance of classification and clustering algorithms. Metric learning algorithms attempt to improve performance, by learning a more appropriate metric. Unfortunately, most of the current algorithms learn...
Feature Selection and Pedestrian Detection Based on Sparse Representation.
PloS one
Aug 21, 2015
Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. ...
Sample Selection for Training Cascade Detectors.
PloS one
Jul 21, 2015
Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represe...
Context-dependent effects of built environment factors on pedestrian-injury severities with imbalanced and high dimensional crash data.
Accident; analysis and prevention
Aug 1, 2025
Built environment is an important component that influences pedestrian injury severities in pedestrian-vehicle crashes. Previous studies indicated that the effects of various built environment factors on pedestrian injury severities are heterogeneous...
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