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Gaze-based Intention Anticipation over Driving Manoeuvres in Semi-Autonomous Vehicles

Min Wu, Tyron Louw, Morteza Lahijanian, Wenjie Ruan, Xiaowei Huang, Natasha Merat, Marta Kwiatkowska
2019 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
The method models human intention as the latent states of a hidden Markov model and uses probabilistic dynamic time warping distributions to capture the temporal characteristics of the observation patterns  ...  The method is evaluated on a data set of 124 experiments from 75 drivers collected in a safety-critical semi-autonomous driving scenario.  ...  In the context of semi-autonomous driving, some previous works focused on lane change recognition based on various data sources [4] - [7] .  ... 
doi:10.1109/iros40897.2019.8967779 dblp:conf/iros/WuLLRHMK19 fatcat:cugu4ta22fhw3f5y7dfskjckwa

Detecting Human Driver Inattentive and Aggressive Driving Behavior using Deep Learning: Recent Advances, Requirements and Open Challenges

Monagi H. Alkinani, Wazir Zada Khan, Quratulain Arshad
2020 IEEE Access  
After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction  ...  For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020  ...  ACKNOWLEDGMENT The authors acknowledge the technical and financial support of University of Jeddah.  ... 
doi:10.1109/access.2020.2999829 fatcat:5nxtzm6yfbe4jf6nqgreqw45r4

Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network [article]

Wenchao Ding and Jing Chen and Shaojie Shen
2019 arXiv   pre-print
We adopt a recurrent neural network (RNN) for observation encoding, and based on that, we propose a novel vehicle behavior interaction network (VBIN) to capture the vehicle interaction from the hidden  ...  accuracy and advanced capability for interaction modeling.  ...  The difference is that CLSTM uses convolutional layers for the pooled hidden states. • Semantic-based intention and motion prediction (SIMP).  ... 
arXiv:1903.00848v2 fatcat:ownit44im5ek7euz4y5x3f3lru

ADMT: Advanced Driver's Movement Tracking system using Spatio-temporal interest points and maneuver anticipation using deep neural networks

Shilpa Gite, Biswajeet Pradhan, Abdullah Alamri, Ketan Kotecha
2021 IEEE Access  
The fusion of extracted features is done to get the probability of driver maneuver.  Hidden Markov Model (HMM): HMM is a probabilistic classifier with one hidden layer but considers the only current context  ...  If he gets lane change assistance while driving, then the possible accidents can be reduced drastically [4] .  ...  CONFLICT OF INTEREST The authors declare no conflict of interest.  ... 
doi:10.1109/access.2021.3096032 fatcat:mcuy36ydyfaynp5itq6ft6txvi

Context Model for Pedestrian Intention Prediction using Factored Latent-Dynamic Conditional Random Fields [article]

Satyajit Neogi, Michael Hoy, Kang Dang, Hang Yu, Justin Dauwels
2019 arXiv   pre-print
We stress on the necessity of early prediction for smooth operation of such systems. We introduce the influence of vehicle interactions on pedestrian intention for this purpose.  ...  Such systems call for early and accurate prediction of a pedestrian's crossing/not-crossing behaviour in front of the vehicle.  ...  We are very thankful to all the reviewers for their valuable feedback.  ... 
arXiv:1907.11881v2 fatcat:zyf2gdi6rbed3f42wbqi2wehv4

Intention based Comparative Analysis of Human-Robot Interaction

Muhammad Awais, Muhammad Yahya Saeed, Muhammad Sheraz Arshad Malik, Muhammad Younas, Rao Sohail Iqbal Asif
2020 IEEE Access  
[37] proposed the use of object affordance for intention understanding using the Hidden Markov Model (HMM). They used the scenario of the assembly line.  ...  The modeled intention corresponded to the lane change during driving. The time-series driving data was modeled using a novel ensemble bi-directional RNN along with Long Short Term Networks (LSTM).  ...  He also have influenced on algorithms designs. He is working as a lecturer in gc university from last seven years with analysing different models designing and verification.  ... 
doi:10.1109/access.2020.3035201 fatcat:cjsfymmmjfdwpmguw6j2aeeknu

Review of the State-of-the-art on Bio-signal-based Brain-controlled Vehicles [article]

Amin Hekmatmanesh, Pedro H. J. Nardelli, Heikki Handroos
2020 arXiv   pre-print
For instance, EEG-based algorithms detect patterns from motor imaginary cortex area of the brain for intention detection, patterns like event related desynchronization/event related synchronization, state  ...  This review focuses on the most relevant topics on the brain controlling vehicles, especially considering terrestrial BCV (e.g., mobile car, car simulators, real car, graphical and gaming cars) and aerial  ...  The HMM is an unsupervised clustering, which is the extension of the Markov Model (MM), that the principals are based on the Markov Chain (MC).  ... 
arXiv:2006.02937v1 fatcat:fcorkhuninazrm5eubvw3y5tta

Pedestrian Models for Autonomous Driving Part II: high level models of human behaviour [article]

Fanta Camara, Nicola Bellotto, Serhan Cosar, Florian Weber, Dimitris Nathanael, Matthias Althoff, Jingyuan Wu, Johannes Ruenz, André Dietrich, Gustav Markkula, Anna Schieben, Fabio Tango, Natasha Merat (+1 others)
2020 arXiv   pre-print
At these levels, early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control.  ...  This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question  ...  [20] proposed a learning method for human motion recognition using EM (Expectation maximization) algorithm and Hidden Markov Model (HMM) for clustering different trajectories and incorporating them  ... 
arXiv:2003.11959v1 fatcat:acjjwohahvdlxgy56j45fjtkdq

Explainable artificial intelligence for autonomous driving: An overview and guide for future research directions [article]

Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel
2022 arXiv   pre-print
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.  ...  Shahin Atakishiyev also acknowledges support from the Ministry of Education of the Republic of Azerbaijan.  ... 
arXiv:2112.11561v2 fatcat:zluqlvmtznh25eihtouubib3ba

2020 Index IEEE Transactions on Intelligent Transportation Systems Vol. 21

2020 IEEE transactions on intelligent transportation systems (Print)  
., +, TITS Dec. 2020 5094-5109 Hidden Markov models Prediction Performance of Lane Changing Behaviors: A Study of Combining Environmental and Eye-Tracking Data in a Driving Simulator.  ...  Chang, B., +, TITS Feb. 2020 852-866 Contextual Recurrent Predictive Model for Long-Term Intent Prediction of Vulnerable Road Users.  ...  R Radar clutter Outliers-Robust CFAR Detector of Gaussian Clutter Based on the Truncated-Maximum-Likelihood-Estimator in SAR Imagery. Ai, J., 2039 -2049  ... 
doi:10.1109/tits.2020.3048827 fatcat:ab6he3jkfjboxg7wa6pagbggs4

Context Aware Control Systems: An Engineering Applications Perspective

Ricardo Alfredo Cajo Diaz, Mihaela Ghita, Dana Copot, Isabela Roxana Birs, Cristina Muresan, Clara Ionescu
2020 IEEE Access  
Based on the driving context information, CASs adapt to the changing driving events.  ...  The method infers a decision based on the probabilities associated with the real facts, handling uncertainty. Examples: Dempster-Shafer, Hidden Markov Models (HMM) and Naïve Bayes; • Ontology-based.  ...  Currently, her research interests focus on novel tools for modeling and control of dynamical systems with fractional calculus tools.  ... 
doi:10.1109/access.2020.3041357 fatcat:774abyybrbb4hcxtfepj2xngd4

A Review of Machine Learning and IoT in Smart Transportation

Fotios Zantalis, Grigorios Koulouras, Sotiris Karabetsos, Dionisis Kandris
2019 Future Internet  
From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications.  ...  With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city.  ...  Markov Models Markov Models are stochastic sequence models based on probability distribution. The simplest Markov model is a Markov chain.  ... 
doi:10.3390/fi11040094 fatcat:6xneyx7ynrgn7p2yl5efy76cee

Gaze and Eye Tracking: Techniques and Applications in ADAS

Khan, Lee
2019 Sensors  
Tracking drivers' eyes and gazes is a topic of great interest in the research of advanced driving assistance systems (ADAS).  ...  A discussion on the required features of current and future eye and gaze trackers is also presented.  ...  For instance, early detection of intention to change the lane was achieved in [221] using HMM-based steering behavior models.  ... 
doi:10.3390/s19245540 pmid:31847432 pmcid:PMC6960643 fatcat:5ujzwbbr65dmtpebey2t6bmk5u

Resource-Constrained Machine Learning for ADAS: A Systematic Review

Juan Borrego-Carazo, David Castells-Rufas, Ernesto Biempica, Jordi Carrabina
2020 IEEE Access  
These methods mainly focus on specific problems ranging from traffic sign and light recognition to pedestrian detection.  ...  In this paper, a survey in the form of systematic review is conducted to analyze the scope of the published research works that embed ML models into resource-constrained implementations for ADAS applications  ...  In it, authors apply Spectral Subtraction (SS) for removing noise in the signal and semi-continuous Hidden Markov Models for the recognition of different words.  ... 
doi:10.1109/access.2020.2976513 fatcat:mgoek62t6zhp3hikgqv36ibpua

Human Motion Trajectory Prediction: A Survey [article]

Andrey Rudenko, Luigi Palmieri, Michael Herman, Kris M. Kitani, Dariu M. Gavrila, Kai O. Arras
2019 arXiv   pre-print
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
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