ArticleSensors (Basel, Switzerland)2016
A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Brain connectivity changes associated with acute sleep deprivation: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- Do occupational health and safety tools that utilize artificial intelligence have a measurable impact on worker injury or illness? Findings from a systematic review.Systematic reviews · 2025Pooled it
- Cutting-edge computational approaches to plant phenotyping.Plant molecular biology · 2025Review
- EdgeNet: An End-to-End Deep Neural Network Pretrained with Synthetic Data for a Real-World Autonomous Driving Application.Sensors (Basel, Switzerland) · 2024Article
- Modelling reindeer rut activity using on-animal acoustic recorders and machine learning.Ecology and evolution · 2024Article
- MRD-YOLO: A Multispectral Object Detection Algorithm for Complex Road Scenes.Sensors (Basel, Switzerland) · 2024Article
- Automated Dimension Estimation of Steel Pipes Stacked at Construction Sites Using Euclidean Distances Calculated by Overlapping Segmentation.Sensors (Basel, Switzerland) · 2022Article
- An Unsupervised Transfer Learning Framework for Visible-Thermal Pedestrian Detection.Sensors (Basel, Switzerland) · 2022Article
- Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks.Sensors (Basel, Switzerland) · 2020Article
- Using a Rotating 3D LiDAR on a Mobile Robot for Estimation of Person's Body Angle and Gender.Sensors (Basel, Switzerland) · 2020Article
- Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios.Sensors (Basel, Switzerland) · 2020Article
- A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research.Sensors (Basel, Switzerland) · 2019Review
- Field-Based High-Throughput Phenotyping for Maize Plant Using 3D LiDAR Point Cloud Generated With a "Phenomobile".Frontiers in plant science · 2019Article
- Article
- Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.GigaScience · 2017Review
- Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System.Sensors (Basel, Switzerland) · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.