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Predicting Hazardous Driving Events Using Multi-Modal Deep Learning Based on Video Motion Profile and Kinematics Data
2018
2018 21st International Conference on Intelligent Transportation Systems (ITSC)
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In this study, we develop a model based on a low-definition driving record instrument and the vehicle kinematic data for postaccident analysis by multi-modal deep learning method. ...
Finally, a multi-modal deep convolutional neural network (DCNN) combined both video and kinematic data is developed to identify potential collision risk in each 12-second vehicle trip. ...
Engineering Laboratory for Integrated Optimization of Road Traffic and Safety Analysis Technologies, and Chinese 111 Project (B17032). ...
doi:10.1109/itsc.2018.8569659
dblp:conf/itsc/GaoLZYWS18
fatcat:amv4u5gtvnacrcr4vqjuowaehi
Pedestrian Models for Autonomous Driving Part II: high level models of human behaviour
[article]
2020
arXiv
pre-print
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Unlike static and kinematic obstacles, pedestrians are active agents with complex, interactive motions. ...
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 ...
The trajectory prediction is modeled as a goal-oriented motion planning. The whole system is based on deep-learning and trained via inverse reinforcement learning. ...
arXiv:2003.11959v1
fatcat:acjjwohahvdlxgy56j45fjtkdq
Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review
[article]
2022
arXiv
pre-print
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This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated ...
estimation, event analysis, modeling and anomaly detection. ...
Jason Pacheco, Larry Head, and Junsuo Qu from their thoughtful comments on this paper. Special thanks go to Greg Leeming from Intel for his insightful comments and continued support of this project. ...
arXiv:2203.10939v1
fatcat:h4o5zghhhfezncn7luy56yjusm
Detection of Physical Strain and Fatigue in Industrial Environments Using Visual and Non-Visual Low-Cost Sensors
2022
Technologies
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information, the correlation and fusion of these estimations with synchronous worker heart rate data, as well as the prediction of near-future heart rate using deep learning-based techniques. ...
Moreover, a new multi-modal dataset of video and heart rate data captured in a real manufacturing workplace during car door assembly activities is introduced. ...
Acknowledgments: The authors thank Consortium partner Stellantis-Centro Ricerche FIAT (CRF)/ SPW Research & Innovation department in Melfi, Italy, for their valuable feedback in the implementation and ...
doi:10.3390/technologies10020042
fatcat:v5c6zwqueneqdbq6exsjxzt4im
Table of Contents
2022
IEEE Robotics and Automation Letters
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Ren, and S. Yang Multi-Modal Model Predictive Control Through Batch Non-Holonomic Trajectory Optimization: Application to Highway Driving . . . . . . . . . . . . . . . . . . . . . . . . . V. K. ...
Havoutis Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/lra.2022.3165102
fatcat:enjzebowe5hn7hsfwklc7nieuy
Unmanned Aerial Vehicle for Remote Sensing Applications—A Review
2019
Remote Sensing
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simultaneously using multi-sensor data for fusion. ...
Based on these solutions, we provide a brief summary of existing examples of UAV-based RS in agricultural, environmental, urban, and hazards assessment applications, etc., and by discussing their practical ...
Acknowledgments: This work was established at the Geospatial Data Analytics (GDA) group in the Department of Civil, Environmental and Geodetic Engineering (CEGE) at the Ohio State University (OSU). ...
doi:10.3390/rs11121443
fatcat:qlo6rpg2s5f6jd4lmy3ypvp4wq
Human Motion Trajectory Prediction: A Survey
[article]
2019
arXiv
pre-print
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We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual ...
Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. ...
Lilienthal for valuable feedback and suggestions. ...
arXiv:1905.06113v3
fatcat:cnomix2fs5gqvb6ormldgti2bm
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
[article]
2021
arXiv
pre-print
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Through extensive evaluations, we show that our model demonstrates significantly more diverse and sample-efficient motion forecasting in a large-scale self-driving dataset as well as safer and less-conservative ...
In particular, we learn a diverse joint distribution over multi-agent future trajectories in a traffic scene that covers a wide range of future modes with high sample efficiency while leveraging the expressive ...
Baselines: For motion forecasting, we use state-of-the-art baselines in multi-modal and diverse prediction, all of them trained end-to-end with the same backbone network and object detector architectures ...
arXiv:2101.06547v3
fatcat:gmgk2i2ghrf7lojthkl2pw5gzq
Main Achievements of the Multidisciplinary SINAPS@ Research Project: Towards an Integrated Approach to Perform Seismic Safety Analysis of Nuclear Facilities
2019
Pure and Applied Geophysics
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of models available to describe physical phenomena (i.e., prediction of seismic motion, site effects, soil and structure interactions, linear and nonlinear wave propagation, material constitutive laws ...
The main lessons learned from SINAPS@ are highlighted. ...
The authors thank all of the SINAPS@ contributors, reseachers, engineers, PhD students, post-doctoral researchers, internships and technicians: the content of this article is based on their work. ...
doi:10.1007/s00024-019-02194-4
fatcat:izvgve7255ed7pltvrw7cd25yy
Autonomous Driving in Adverse Weather Conditions: A Survey
[article]
2021
arXiv
pre-print
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a holistic overview on the obstacles and directions of ADS development in terms of adverse weather driving conditions. ...
Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. ...
Pitropov et al. [312]
and multi-layer deep learning approaches [292] are used presented the first AV dataset that focuses on snow con-
in pedestrian detection tasks in bad weather. ...
arXiv:2112.08936v1
fatcat:hmgjhywy7rgx3fgrk6yxnu56ie
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
[article]
2021
arXiv
pre-print
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; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. ...
We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning ...
Institute for Occupational Safety and Health, Polytechnique Montreal, and West Virginia University. ...
arXiv:2103.11470v4
fatcat:rq2wgczzl5clphzgujjdbd3ywm
Modeling and Simulation of Sport Games, Sport Movements, and Adaptations to Training (Dagstuhl Seminar 15382)
2016
Dagstuhl Reports
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For instance, computational models are applied in motor control and learning, biomechanics, game analysis, training science, sport psychology, and sport sociology. ...
, Ricardo Duarte, Björn Eskofier, and Martin Rumpf Computational modeling and simulation are essential to analyze human motion and interaction in sport science, sport practice and sport industry. ...
Two sensing modalities are in use for field studies of human movement: inertial sensing and video. ...
doi:10.4230/dagrep.5.9.38
dblp:journals/dagstuhl-reports/DuarteERW15
fatcat:bavxhyhkwfc57dtt7nux7wjoea
CARS 2020—Computer Assisted Radiology and Surgery Proceedings of the 34th International Congress and Exhibition, Munich, Germany, June 23–27, 2020
2020
International Journal of Computer Assisted Radiology and Surgery
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With an increasing general demand and pressure on CARS to also go fully digital in the long term, many members of the CARS Congress Organizing Committee, however, are more cautious and convinced that one ...
Aiming to stimulate complimentary thoughts and actions on what is being presented at CARS, implies a number of enabling variables for optimal analogue scholarly communication, such as (examples given are ...
Kimura and Dr. Nagaoka (BOST, Kindai University) for their technical assistance. The work is supported by a grant-in-aid for scientific research on innovative areas, JSPS KAKENHI 17K17680. ...
doi:10.1007/s11548-020-02171-6
pmid:32514840
fatcat:lyhdb2zfpjcqbf4mmbunddwroq
Visual Analysis in Traffic & Re-identification
[article]
2015
Ph.d.-serien for Det Teknisk-Naturvidenskabelige Fakultet, Aalborg Universitet
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Acknowledgment The authors would like to thank their colleagues at the LISA lab for useful discussion and encouragement, especially Eshed Ohn-Bar for his valuable comments. ...
Sujitha Martin, and Mr. Eshed Ohn-Bar for their comments. ...
As opposed to the works described above, in this paper we introduce a truly multi-modal approach based on RGB, depth and thermal data. ...
doi:10.5278/vbn.phd.engsci.00026
fatcat:taivrerts5debi734ddeeaq244
Deep learning-based ambient assisted living for self-management of cardiovascular conditions
2021
Neural computing & applications (Print)
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learning strategies can be used to improve the medical services. ...
The paper is divided into four main themes, including self-monitoring wearable systems, ambient assisted living in aged populations, clinician management systems and deep learning-based systems for cardiovascular ...
Deep learning methods are helpful for better and superior performance, integration, feature learning, handling complex and multi-modality data. ...
doi:10.1007/s00521-020-05678-w
fatcat:fkabjm33xza2ncgq6ecu2qifaq
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