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TOWARD FLEXIBLE DATA COLLECTION OF DRIVING BEHAVIOUR

M. Ameksa, H. Mousannif, H. Al Moatassime, Z. Elamrani Abou Elassad
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Recently, driving behavior has been the focus of several researchers and scientists, they are attempting to identify and analyze driving behavior using different sources of data.  ...  Using a systematic literature review strategy, this study identified tools and techniques used to collect data related to driving behavior among 120 selected studies from 2010 to 2020 in several literature  ...  Cruise Control design based on human driving data Self-report Other sensors smartphone Traffic surv Dataset Driver Vehicle Environment S1 Driving Behavior Inference from Traffic Surveillance  ... 
doi:10.5194/isprs-archives-xliv-4-w3-2020-33-2020 fatcat:elhcwaqupbg4npaprz3a3kvowy

Intelligent Surveillance for Understanding Events in Urban Traffic Environments

D. Vallejo, F. J. Villanueva, J. A. Albusac, C. Glez-Morcillo, J. J. Castro-Schez
2014 International Journal of Distributed Sensor Networks  
Two case studies of urban traffic environments are discussed to prove the feasibility of the proposal.  ...  Agents deployed by means of this platform implement a behavior-based model that is flexible enough to deal with the challenges that monitored environments pose.  ...  Since intelligent surveillance may involve a large number of moving objects whose behavior must be analyzed from the data obtained from multiple sensors, a multiagent based approach fits very well to deal  ... 
doi:10.1155/2014/723819 fatcat:ozibpxs2fbgkhortzovudfxace

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

Bin Tian, Brendan Tran Morris, Ming Tang, Yuqiang Liu, Yanjie Yao, Chao Gou, Dayong Shen, Shaohu Tang
2017 IEEE transactions on intelligent transportation systems (Print)  
Traffic surveillance has become an important topic in intelligent transportation systems (ITSs), which is aimed at monitoring and managing traffic flow.  ...  With the progress in computer vision, video-based surveillance systems have made great advances on traffic surveillance in ITSs.  ...  From a massive amount of behavior research, there are two main ways to understand vehicle behaviors in traffic surveillance systems.  ... 
doi:10.1109/tits.2016.2552778 fatcat:hkc7ug3rgballl4gtfp2a2nu2i

Hierarchical and Networked Vehicle Surveillance in ITS: A Survey

Bin Tian, Brendan Tran Morris, Ming Tang, Yuqiang Liu, Yanjie Yao, Chao Gou, Dayong Shen, Shaohu Tang
2014 IEEE transactions on intelligent transportation systems (Print)  
Traffic surveillance has become an important topic in intelligent transportation systems (ITSs), which is aimed at monitoring and managing traffic flow.  ...  With the progress in computer vision, video-based surveillance systems have made great advances on traffic surveillance in ITSs.  ...  From a massive amount of behavior research, there are two main ways to understand vehicle behaviors in traffic surveillance systems.  ... 
doi:10.1109/tits.2014.2340701 fatcat:ibm5rputr5gv5o2kazayjr6wkq

Observing on-road vehicle behavior: Issues, approaches, and perspectives

Sayanan Sivaraman, Brendan Morris, Mohan Trivedi
2013 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)  
The ITS community has approached this topic both from vehicle-based and infrastructure-based sensing.  ...  This review focuses on vision-based sensing and provides highlights of state-of-the art methods used in surveillance, and on-road vision modalities.  ...  A key constraint in behavior understanding algorithm design from infrastructure video arises from far-field surveillance.  ... 
doi:10.1109/itsc.2013.6728485 dblp:conf/itsc/SivaramanMT13 fatcat:vjp5qicfwvhy7epupfvm5vaera

Extraction and Assessment of Naturalistic Human Driving Trajectories from Infrastructure Camera and Radar Sensors [article]

Dominik Notz, Felix Becker, Thomas Kühbeck, Daniel Watzenig
2020 arXiv   pre-print
This influence prevents recording the whole traffic space of human driving behavior.  ...  Collecting realistic driving trajectories is crucial for training machine learning models that imitate human driving behavior.  ...  like to thank Anthony Acker, Andrew Dickens, Mehmet Inönü, Sebastian Kienitz, Florian Münch, and Brad Siedner for their support with the construction of the hardware prototype and the execution of the data  ... 
arXiv:2004.01288v1 fatcat:oitbgzsonnbl7nl7madverj5xy

Probabilistic Vehicular Trace Reconstruction Based on RF-Visual Data Fusion [chapter]

Saif Al-Kuwari, Stephen D. Wolthusen
2010 Lecture Notes in Computer Science  
The algorithm provides a probabilistic treatment to the problem of incomplete data by means of Bayesian inference.  ...  data.  ...  Based on a basic Bayesian inference approach and the driving behaviors of the target (obtained from the available traces), these routes are analyzed and the most probable one is selected.  ... 
doi:10.1007/978-3-642-13241-4_3 fatcat:hxit7tph7bdgjjn6hh5np5pgza

Scanning the Issue

Petros Ioannou, A. V. Bal Balakrishnan
2018 IEEE transactions on intelligent transportation systems (Print)  
and surveillance-nature data.  ...  The inferred track and behavior of other vehicles can be used to perform safe actions in intelligent vehicle applications and autonomous driving.  ... 
doi:10.1109/tits.2018.2866887 fatcat:3brvnbxecnegbdwnf5f56cg4ey

DeepFlow: Abnormal Traffic Flow Detection Using Siamese Networks [article]

Sepehr Sabour, Sanjeev Rao, Majid Ghaderi
2021 arXiv   pre-print
Our model can detect abnormal traffic flows by analyzing the trajectory data collected from the vehicles in a fleet. To evaluate DeepFlow, we use realistic vehicular traffic simulations in SUMO.  ...  Nowadays, many cities are equipped with surveillance systems and traffic control centers to monitor vehicular traffic for road safety and efficiency.  ...  This data can be used to analyze traffic flows and driver behavior in order to detect abnormal driving patterns.  ... 
arXiv:2108.12016v1 fatcat:p4dl3q6a3jchnpm3ku4o4zl7hq

Understanding vehicular traffic behavior from video: a survey of unsupervised approaches

Brendan Tran Morris, Mohan Manubhai Trivedi
2013 Journal of Electronic Imaging (JEI)  
Recent emerging trends for automatic behavior analysis and understanding from infrastructure video are reviewed.  ...  Example applications that utilize the behavioral modeling techniques are also presented. In addition, the most popular public datasets for behavioral analysis are presented.  ...  The low-level actions and temporal order within high-level behaviors are inferred directly from trajectories.  ... 
doi:10.1117/1.jei.22.4.041113 fatcat:ftd2elgj5vd4vaada3hxfsfiby

Glyph-based video visualization on Google Map for surveillance in smart cities

Fozia Mehboob, Muhammad Abbas, Saad Rehman, Shoab A. Khan, Richard Jiang, Ahmed Bouridane
2017 EURASIP Journal on Image and Video Processing  
This form of traffic visualization can potentially reduce the data complexity, having holistic view from larger collection of videos.  ...  In this paper, a state of the art algorithm has been proposed for 3D conversion from traffic video content to Google Map.  ...  When visualizing traffic situations, they are ineffective to simulate all kinds of dynamics and kinematics due to unknown driving behavior in different regions.  ... 
doi:10.1186/s13640-017-0175-4 fatcat:bw3qxna4i5dpbbjqgs7f7oqe3q

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Zhensong Wei, Chao Wang, Peng Hao, Matthew J. Barth
2019 2019 IEEE Intelligent Transportation Systems Conference (ITSC)  
These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs.  ...  Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s.  ...  DRIVING BEHAVIOR MODELING Modeling and predicting the driving behavior of conventional human-driven vehicles are essential for designing the motion behavior of CAVs in mixed traffic conditions.  ... 
doi:10.1109/itsc.2019.8917158 dblp:conf/itsc/WeiWHB19 fatcat:ng6whh4j25gaviaovf2y7gu5sy

The State-of-the-Art of Coordinated Ramp Control with Mixed Traffic Conditions

Zhouqiao Zhao, Ziran Wang, Guoyuan Wu, Fei Ye, Matthew J. Barth
2019 2019 IEEE Intelligent Transportation Systems Conference (ITSC)  
These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs.  ...  Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s.  ...  DRIVING BEHAVIOR MODELING Modeling and predicting the driving behavior of conventional human-driven vehicles are essential for designing the motion behavior of CAVs in mixed traffic conditions.  ... 
doi:10.1109/itsc.2019.8917067 dblp:conf/itsc/ZhaoWWYB19 fatcat:tocg6ophqjghbom6rbli6iusb4

Driving Behavior Assessment and Anomaly Detection for Intelligent Vehicles

Chule Yang, Alessandro Renzaglia, Anshul Paigwar, Christian Laugier, Danwei Wang
2019 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM)  
Finally, by chaining these observation states using a Markov model, the abnormality of driving behavior can be inferred into Normal, Attention, and Risk.  ...  To this purpose, intelligent vehicles not only have to drive safe but must be able to safeguard itself from other abnormally driving vehicles and avoid potential collisions.  ...  Few works focus on associating the anomalous driving maneuver with the behavior risk in a traffic scenario.  ... 
doi:10.1109/cis-ram47153.2019.9095790 dblp:conf/ram/YangRPLW19 fatcat:7mctznxzrnbg7lioisl3toaga4

Artificial Intelligence Applications to Smart City and Smart Enterprise

Donato Impedovo, Giuseppe Pirlo
2020 Applied Sciences  
Published works refer to the following areas of interest: vehicular traffic prediction; social big data analysis; smart city management; driving and routing; localization; and safety, health, and life  ...  [10] implemented two kinds of deep learning techniques to reflect human driving behavior for automated car driving.  ...  In reference [12] , authors propose a methodology to detect texting and driving behavior of drivers.  ... 
doi:10.3390/app10082944 fatcat:rbz3kszunzfyvmleusroxm7hhy
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