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Instance Segmentation in CARLA: Methodology and Analysis for Pedestrian-oriented Synthetic Data Generation in Crowded Scenes

Maria Lyssenko, Christoph Gladisch, Christian Heinzemann, Matthias Woehrle, Rudolph Triebel
2021 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)  
the context of crowded scenes in urban automated driving.  ...  Synthetic data from 3D simulators like CARLA may provide a solution to this problem as labeled data can be produced in a structured manner.  ...  Since our instance segmentation methodology delivers precise per-pedestrian annotations and distances for dense scenes with a high degree of overlap, this approach can be used to generate large-scale high-quality  ... 
doi:10.1109/iccvw54120.2021.00115 fatcat:dih5ykkujvfkhjyymcc7elolju

From Evaluation to Verification: Towards Task-oriented Relevance Metrics for Pedestrian Detection in Safety-critical Domains

Maria Lyssenko, Christoph Gladisch, Christian Heinzemann, Matthias Woehrle, Rudolph Triebel
2021 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Our experimental setup is based on the CARLA simulator and allows a controlled evaluation of the impact of that domain knowledge.  ...  In our work, we consider pedestrian detection as a highly relevant perception task, and we argue that standard measures such as Intersection over Union (IoU) give insufficient results, mainly because they  ...  Pedestrian-oriented Simulation Setup In this section we describe our methodology to exploiting synthetically generated pedestrian data from CARLA to introduce an task-oriented relevance metric.  ... 
doi:10.1109/cvprw53098.2021.00013 fatcat:25yh43jjfnefnipvdonh5rdyma

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

Monirul Islam Pavel, Siok Yee Tan, Azizi Abdullah
2022 Applied Sciences  
, pedestrian detection, lane and curve detection, road object localization, traffic scene analysis), decision making, end-to-end controlling and prediction, path and motion planning and augmented reality-based  ...  navigation and enhanced safety with overlapping on vehicles and pedestrians in extreme visual conditions to reduce collisions.  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12146831 fatcat:qkeylw67sngrtmmgwa2r3ue3ii

Synthetic Data for Deep Learning [article]

Sergey I. Nikolenko
2019 arXiv   pre-print
for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, simulation environments for robotics, applications of synthetic data outside computer vision (in  ...  First, we discuss synthetic datasets for basic computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., semantic segmentation), synthetic environments and datasets  ...  This ties into crowd analysis, where synthetic data is used to model crowds and train visual crowd analysis tools on rendered images [549] . Huang et al.  ... 
arXiv:1909.11512v1 fatcat:qquxnw4dfvgmfeztbpdqhr44gy

Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art [article]

Joel Janai, Fatma Güney, Aseem Behl, Andreas Geiger
2021 arXiv   pre-print
While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published.  ...  understanding, and end-to-end learning for autonomous driving.  ...  Carla allows generating synthetic data for control and perception of an autonomous driving system in urban environments.  ... 
arXiv:1704.05519v3 fatcat:xiintiarqjbfldheeg2hsydyra

CarMap: Fast 3D Feature Map Updates for Automobiles

Fawad Ahmad, Hang Qiu, Ray Eells, Fan Bai, Ramesh Govindan
2020 Symposium on Networked Systems Design and Implementation  
, and incorporates a novel stitching algorithm to combine map segments from multiple vehicles for unmapped road segments and an efficient map-update operation for updating existing segments.  ...  In this paper, we explore a different approach: near-real time crowd-sourced 3D map collection from vehicles with advanced sensors (LiDAR, stereo cameras).  ...  Our shepherd Kyle Jamieson and the anonymous reviewers provided valuable feedback. The work was supported by grants from the US National Science Foundation (Grant No. CNS-1330118) and General Motors.  ... 
dblp:conf/nsdi/AhmadQEBG20 fatcat:uxpianfigbeyvpmakk7bphwfn4

The State of Modeling, Simulation, and Data Utilization within Industry: An Autonomous Vehicles Perspective [article]

Joshua Fadaie
2019 arXiv   pre-print
In particular, the future growth and disruptive ability of smart cities, autonomous vehicles and in general, urban mobility, hinges on the development of state of the art simulation tools and the intelligent  ...  In order for aviation based companies to adequately pursue disruptive mobility within real-world environments, be it in air or on the ground, modeling and simulation tools for autonomous vehicles provide  ...  Object Oriented Simulation Programming Object oriented programming is a traditional development methodology used for interacting with, and constructing the simulated environment and underlying models.  ... 
arXiv:1910.06075v1 fatcat:k7d2tuj3lfer3c7psxm7pwgb3m

From Recognition to Prediction: Analysis of Human Action and Trajectory Prediction in Video [article]

Junwei Liang
2021 arXiv   pre-print
Many systems do not provide high-level semantic attributes to reason about pedestrian future. This design hinders prediction performance in video data from diverse domains and unseen scenarios.  ...  To enable optimal future human behavioral forecasting, it is crucial for the system to be able to detect and analyze human activities as well as scene semantics, passing informative features to the subsequent  ...  Following the success of learning from simulation [45, 57, 182, 187, 229, 263] , our synthetic data is generalized from a 3D simulator, called CARLA [50] , which anchors to the static scene and dynamic  ... 
arXiv:2011.10670v3 fatcat:mlom5zqk6jdvjndcsfwimpj7xu

Test Your Self-Driving Algorithm: An Overview of Publicly Available Driving Datasets and Virtual Testing Environments

Yue Kang, Hang Yin, Christian Berger
2019 IEEE Transactions on Intelligent Vehicles  
His research expertise is on distributed realtime software, microservices for embedded systems and cyberphysical systems, and continuous integration/deployment/experimentation for embedded systems.  ...  Benderius and Y. Yu for their valuable comments.  ...  It was studied how patterns from virtual testing data can be matched in data recordings from real sensors with the goal of finding interesting scenarios in reality for further analysis.  ... 
doi:10.1109/tiv.2018.2886678 fatcat:yxvl56ktwfgklielhdvm6234su

Scanning the Issue

Azim Eskandarian
2022 IEEE transactions on intelligent transportation systems (Print)  
This article investigates the influence of an external humanmachine interface (eHMI) for automated vehicles on pedestrian behavior in a parking lot.  ...  In total, 90 subjects from China, South Korea, and the USA assume a pedestrian's role in a virtual reality-based pedestrian simulator and experience three encounter scenarios with an automated vehicle.  ...  Xiong, and C. Shen Counting crowd and vehicles in transportation is important for avoiding crowd stampedes and traffic jams.  ... 
doi:10.1109/tits.2022.3160062 fatcat:4gklzaonfzcehnvps6oge35fwe

Analysis of the Influence of Training Data on Road User Detection

Carlos Guindel, David Martin, Jose Maria Armingol, Christoph Stiller
2018 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)  
Stiller and the other members of the MRT team for the four months that I spent there.  ...  I beg the English-speaking readers to understand my decision to write these lines in my native language, Spanish. Before that, however, I would like to thank Prof.  ...  The stereo system was used for lane recognition and 3D scene analysis; the forward-looking wideangle camera was aimed at traffic light and pedestrian recognition in turning maneuvers; and finally, the  ... 
doi:10.1109/icves.2018.8519510 dblp:conf/icves/Guindel0AS18 fatcat:fu3cvrvkdjeivndsipd4hnhnqy

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey [article]

Julian Wörmann, Daniel Bogdoll, Etienne Bührle, Han Chen, Evaristus Fuh Chuo, Kostadin Cvejoski, Ludger van Elst, Tobias Gleißner, Philip Gottschall, Stefan Griesche, Christian Hellert, Christian Hesels (+34 others)
2022 arXiv   pre-print
However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training.  ...  Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models  ...  They introduce a cost function so that the different efforts for manually labeling sparse or crowded scenes are reflected and fix a cost bound per iteration.  ... 
arXiv:2205.04712v1 fatcat:u2bgxr2ctnfdjcdbruzrtjwot4

Surgical Data Science - from Concepts toward Clinical Translation

Lena Maier-Hein, Matthias Eisenmann, Duygu Sarikaya, Keno März, Toby Collins, Anand Malpani, Johannes Fallert, Hubertus Feussner, Stamatia Giannarou, Pietro Mascagni, Hirenkumar Nakawala, Adrian Park (+39 others)
2021 Medical Image Analysis  
Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery.  ...  of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics.  ...  Acknowledgments Many thanks to Annika Reinke (DKFZ, Germany) for  ... 
doi:10.1016/j.media.2021.102306 pmid:34879287 pmcid:PMC9135051 fatcat:4n27fogyqndghlc2e54h7uohgq

Surgical Data Science – from Concepts toward Clinical Translation [article]

Lena Maier-Hein, Matthias Eisenmann, Duygu Sarikaya, Keno März, Toby Collins, Anand Malpani, Johannes Fallert, Hubertus Feussner, Stamatia Giannarou, Pietro Mascagni, Hirenkumar Nakawala, Adrian Park (+38 others)
2021 arXiv   pre-print
Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery.  ...  of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics.  ...  Most companies recruit non-specialists who can perform conceptually simple tasks on image and video data, such as urban scene segmentation and pedestrian detection for autonomous driving.  ... 
arXiv:2011.02284v2 fatcat:i5mq42uevjfxjmji5xdap3kgse

An Embarrassingly Pragmatic Introduction to Vision-based Autonomous Robots [article]

Marcos V. Conde
2021 arXiv   pre-print
the environment or scene, move, adapt its trajectory and perform its tasks (maintenance, exploration, etc.) without the need for human intervention.  ...  In this work, we develop a small-scale autonomous vehicle from scratch, capable of understanding the scene using only visual information, navigating through industrial environments, detecting people and  ...  Methodologies from Machine Learning in Data Analysis and Software. The Computer Journal, 34(6):559–565, 12 1991.  ... 
arXiv:2112.05534v2 fatcat:3drhsxelvvdwvpsq5rvfpnukam
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