Human-Like Behaviour for Automated Vehicles (HLB4AV) Naturalistic Driving Dataset

Human-Like Behaviour for Automated Vehicles (HLB4AV) dataset is a collection of data of drivers, driving naturally in a rural road segment. The instructions were always to drive with their own, instinctive style, without knowing the goal of the measurement. The measurements took place always during the day, on weekdays, with no or low traffic, good sight visibilities and dry weather conditions. There are always reference measurements of the given road sections, where professional drivers were asked to drive a well-sensored test vehicle. Dataset contains information of accurate vehicle kinematics, lane edge geometry, test vehicle localization and surrounding traffic. The dataset is unique as besides physical vehicle data metadata of the drivers in terms of driving frequency, experience and age is also added. The data can be used to analize the basic path and speed selection of drivers on rural roads, their reaction on other traffic participants or lane offset selection. The data continuously grows, new measurement platforms as well as new drivers and road sections are planned to be added.

Authors

Publication Details

Published
2024-09-19
DOI
https://doi.org/10.1109/sisy62279.2024.10737549
Citations
1
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.20
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article

Human-Like Behaviour for Automated Vehicles (HLB4AV) Naturalistic Driving Dataset

Gergő Ferenc Ignéczi, Ernő Horváth
1 citations
Autonomous Vehicle Technology and Safety
0.20
article

Human-Like Behaviour for Automated Vehicles (HLB4AV) Naturalistic Driving Dataset

Gergő Ferenc Ignéczi, Ernő Horváth
article en
1 citations

Abstract

Human-Like Behaviour for Automated Vehicles (HLB4AV) dataset is a collection of data of drivers, driving naturally in a rural road segment. The instructions were always to drive with their own, instinctive style, without knowing the goal of the measurement. The measurements took place always during the day, on weekdays, with no or low traffic, good sight visibilities and dry weather conditions. There are always reference measurements of the given road sections, where professional drivers were asked to drive a well-sensored test vehicle. Dataset contains information of accurate vehicle kinematics, lane edge geometry, test vehicle localization and surrounding traffic. The dataset is unique as besides physical vehicle data metadata of the drivers in terms of driving frequency, experience and age is also added. The data can be used to analize the basic path and speed selection of drivers on rural roads, their reaction on other traffic participants or lane offset selection. The data continuously grows, new measurement platforms as well as new drivers and road sections are planned to be added.

Openalex Percentile: Top 49%
Autonomous Vehicle Technology and Safety
0.20
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