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Why did the Robot Cross the Road? - Learning from Multi-Modal Sensor Data for Autonomous Road Crossing
[article]
2017
arXiv
pre-print
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In this work, we propose a novel multi-modal learning approach for the problem of autonomous street crossing. ...
Our approach solely relies on laser and radar data and learns a classifier based on Random Forests to predict when it is safe to cross the road. ...
Meissner et al. use a multi-sensor tracking system for classification of relevant objects [16] . ...
arXiv:1709.06039v1
fatcat:5cpxarxpnbguxolqny6hjmljry
ROAD: The ROad event Awareness Dataset for Autonomous Driving
[article]
2021
arXiv
pre-print
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To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ...
ROAD comprises 22 videos, originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event. ...
The latest generation of robot-cars is equipped with a range of different sensors (i.e., laser rangefinders, radar, cameras, GPS) to provide data on what is happening on the road [6] . ...
arXiv:2102.11585v2
fatcat:k25yvvjkonf33clpioyyf5eola
Color Vision for Road Following
[chapter]
1990
The Kluwer International Series in Engineering and Computer Science
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Reflectance also changes from place to place along the road, as the road surface goes from dirty to clean or from wet to dry. ...
This repon describes progress in vision and navigation for outdoor mobile robots at the Carnegie Mellon Robotics Insfflute during 1988. ...
Keith Grcmban ported and demonstrated the road following program on the Manin Marietta ALV. ...
doi:10.1007/978-1-4613-1533-9_2
fatcat:lqsollwsc5c2lbov2gwlmqkyry
Road and Railway Smart Mobility: A High-definition Ground Truth Hybrid Dataset
2022
Sensors
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A robust visual understanding of complex urban environments using passive optical sensors is an onerous and essential task for autonomous navigation. ...
For this purpose, in order to improve the level of instances in datasets used for the training and validation of Autonomous Vehicles (AV), Advanced Driver Assistance Systems (ADAS), and autonomous driving ...
This is still relatively less time consuming and expensive than building the multi-modal dataset often including range data from LiDAR or Radar [3, 24] . ...
doi:10.3390/s22103922
pmid:35632331
fatcat:gcilmijdtfa35p3scz6bfizd5e
Cost-Efficient Global Robot Navigation in Rugged Off-Road Terrain
2011
Künstliche Intelligenz
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Abstract This thesis addresses the problem of finding a global robot navigation strategy for rugged off-road terrain which is robust against inaccurate self-localization and scalable to large environments ...
And finally, I am very thankful for the unwavering support received from my girlfriend Christiane, who tolerated the many late nights at the lab and constantly provided comfort during difficult times when ...
A probabilistic online learning framework for autonomous off-road robot navigation is presented in [Erkan 07 ]. ...
doi:10.1007/s13218-011-0088-9
fatcat:u4ssainmcvc3jmqdon6gpjg674
Vulnerable road users and the coming wave of automated vehicles: Expert perspectives
2021
Transportation Research Interdisciplinary Perspectives
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Acknowledgements This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860410. ...
These come in different modalities, including LED strips and screens, robotic attachments, projections on the road, and auditory signals, amongst others. ...
For public transport or robot taxis, you can train a vehicle to drive on specific routes. For the general public, this will be interpreted as fully automated or autonomous driving. ...
doi:10.1016/j.trip.2020.100293
fatcat:rwtx64g63ngk5ps6m4elho6qge
Autonomous Driving in Adverse Weather Conditions: A Survey
[article]
2021
arXiv
pre-print
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However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. ...
This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. ...
“Multi-Modal Sensor
[176] Haiyan Wu et al. “Contrastive Learning for Com- Fusion-Based Semantic Segmentation for Snow
pact Single Image Dehazing”. ...
arXiv:2112.08936v1
fatcat:hmgjhywy7rgx3fgrk6yxnu56ie
Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice
[article]
2018
arXiv
pre-print
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The interaction between road users is a form of negotiation in which the parties involved have to share their attention regarding a common objective or a goal (e.g. crossing an intersection), and coordinate ...
More specifically, we will discuss the theoretical background behind joint attention, its application to traffic interaction and practical approaches to implementing joint attention for autonomous vehicles ...
In autonomous driving, different sensor modalities can be used to improve the performance of detection. For instance, Lange et al. ...
arXiv:1802.02522v2
fatcat:nzeq5eleajcktjl32m2kyqu7rq
Pedestrian Models for Autonomous Driving Part II: high level models of human behaviour
[article]
2020
arXiv
pre-print
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Autonomous vehicles (AVs) must share space with human pedestrians, both in on-road cases such as cars at pedestrian crossings and off-road cases such as delivery vehicles navigating through crowds on high-streets ...
level image detection to high-level psychological models, from the perspective of an AV designer. ...
The authors in [120] present a multi-modal dataset for obstacle detection in agriculture. ...
arXiv:2003.11959v1
fatcat:acjjwohahvdlxgy56j45fjtkdq
Deep Learning-Based Frameworks for Semantic Segmentation of Road Scenes
2022
Electronics
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To overcome a lack of enough data required for the training process, data augmentation techniques and their experimental results are reviewed. ...
This paper presents a detailed review of deep learning-based frameworks used for semantic segmentation of road scenes, highlighting their architectures and tasks. ...
[32] and Ros and Alvarez [46] generated the ground truth from the road detection challenge for 323 images with three classes: sky, vertical, and road. Ros et al. ...
doi:10.3390/electronics11121884
fatcat:ekykzfnqtjcbla3fh4vlwhkvgu
Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice
2019
IEEE transactions on intelligent transportation systems (Print)
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To make it a reality, autonomous vehicles require the ability to communicate with other road users and understand their intentions. ...
We will also review the practical applications aimed at solving the interaction problem, including design approaches for autonomous vehicles that communicate with pedestrians and visual perception and ...
The authors use a pre-recorded video of the pedestrians who were instructed to engage in various activities with the robot (e.g. approaching the robot for interaction or simply blocking its way), to learn ...
doi:10.1109/tits.2019.2901817
fatcat:anhktkdjx5hnphwvoksfxthdfi
Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception and Active Vision
2020
IEEE Access
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ACKNOWLEDGMENT This research work is supported by the Academy of Finland's AutoSOS project (Grant No. 328755). ...
data from different modalities to a joint space, alignment, i.e., how to understand the relations of the elements of data from different modalities, for example, which parts of the data describe the same ...
development of multi-modal sensor fusion algorithms. ...
doi:10.1109/access.2020.3030190
fatcat:exigopjplzgfzlghxvr7s3l3di
Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception
[article]
2020
arXiv
pre-print
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In this paper, we review and analyze the existing approaches to multi-robot SAR support, from an algorithmic perspective and putting an emphasis on the methods enabling collaboration among the robots as ...
multi-robot SAR systems. ...
ACKNOWLEDGMENT This research work is supported by the Academy of Finland's AutoSOS project (Grant No. 328755). ...
arXiv:2008.12610v1
fatcat:hq5lqtnsoreapjm4dpgg4z5xki
A2D2: Audi Autonomous Driving Dataset
[article]
2020
arXiv
pre-print
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Research in machine learning, mobile robotics, and autonomous driving is accelerated by the availability of high quality annotated data. ...
In addition, we provide 392,556 sequential frames of unannotated sensor data for recordings in three cities in the south of Germany. These sequences contain several loops. ...
in checking the quality of the dataset. ...
arXiv:2004.06320v1
fatcat:oirikiaxjbb6zn2pieu3qmhkou
Explainable artificial intelligence for autonomous driving: An overview and guide for future research directions
[article]
2022
arXiv
pre-print
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First, we provide a thorough overview of the state-of-the-art studies on XAI for autonomous driving. ...
We then propose an XAI framework that considers all the societal and legal requirements for explainability of autonomous driving systems. ...
ACKNOWLEDGMENT We acknowledge support from the Alberta Machine Intelligence Institute (Amii), from the Computing Science Department of the University of Alberta, and the Natural Sciences and Engineering ...
arXiv:2112.11561v2
fatcat:zluqlvmtznh25eihtouubib3ba
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