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Automatic lane change scenario extraction and generation of scenarios in OpenX format from real-world data

Automatic lane change scenario extraction and generation of scenarios in OpenX format from real-world data

D. Karunakaran
J. S. Berrio
Stewart Worrall
E. Nebot
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Papers citing "Automatic lane change scenario extraction and generation of scenarios in OpenX format from real-world data"

6 / 6 papers shown
Title
The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenX
The ConScenD Dataset: Concrete Scenarios from the highD Dataset According to ALKS Regulation UNECE R157 in OpenX
A. Tenbrock
A. Koenig
Thomas Keutgens
Julian Bock
Hendrik Weber
R. Krajewski
A. Zlocki
41
25
0
17 Mar 2021
Identification of Challenging Highway-Scenarios for the Safety Validation of Automated Vehicles Based on Real Driving Data
Identification of Challenging Highway-Scenarios for the Safety Validation of Automated Vehicles Based on Real Driving Data
Thomas Ponn
Matthias Breitfuss
Xiao Yu
Frank Diermeyer
42
21
0
26 Aug 2020
Real-World Scenario Mining for the Assessment of Automated Vehicles
Real-World Scenario Mining for the Assessment of Automated Vehicles
Erwin de Gelder
J. Manders
Corrado Grappiolo
J. Paardekooper
Olaf Op den Camp
B. de Schutter
39
23
0
31 May 2020
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems
The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems
R. Krajewski
Julian Bock
Laurent Kloeker
L. Eckstein
82
994
0
11 Oct 2018
CARLA: An Open Urban Driving Simulator
CARLA: An Open Urban Driving Simulator
Alexey Dosovitskiy
G. Ros
Felipe Codevilla
Antonio M. López
V. Koltun
VLM
135
5,173
0
10 Nov 2017
Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniques
Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniques
Ding Zhao
Henry Lam
H. Peng
S. Bao
D. Leblanc
Kazutoshi Nobukawa
C. S. Pan
57
355
0
16 May 2016

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