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An improved LSTM Model for Behavior Recognition of Intelligent Vehicles
2020
IEEE Access
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In this paper, LSTM is applied to the vehicle behavior recognition problem to identify the left turn, right turn and straight behavior of the vehicle at the intersection. ...
INDEX TERMS Intelligent vehicles, LSTM. ...
In order to improve the traditional LSTM model, this paper proposes an improved LSTM model for vehicle behavior recognition. ...
doi:10.1109/access.2020.2996203
fatcat:d33mwnpvsjh3healqgncseg3ka
UB-LSTM: A Trajectory Prediction Method Combined with Vehicle Behavior Recognition
2020
Journal of Advanced Transportation
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In order to make an accurate prediction of vehicle trajectory in a dynamic environment, a Unidirectional and Bidirectional LSTM (UB-LSTM) vehicle trajectory prediction model combined with behavior recognition ...
Then, the trajectory prediction model is established based on Unidirectional and Bidirectional LSTM, and the identified vehicle behavior and the input information of the behavior recognition model are ...
In order to make an accurate prediction of moving vehicle trajectory, a UB-LSTM vehicle trajectory prediction model combined with vehicle behavior recognition is proposed to obtain a smaller prediction ...
doi:10.1155/2020/8859689
fatcat:wvpcwypcmbhl3oxhcvarb45rim
Driver Lane Change Intention Recognition of Intelligent Vehicle based on Long Short-Term Memory Network
2020
IEEE Access
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Second, the Multi-LSTM-based prediction controller is constructed to learn vehicle behavior characteristics and time series relation of various states in the process of lane change. ...
INDEX TERMS Intelligent vehicle, lane change, driving intention prediction, advanced assisted driving systems, multi-LSTM. ...
model and improve the robustness for the number of samples. ...
doi:10.1109/access.2020.3011550
fatcat:q6bhmusg3rbknfmgbiiohjsfxa
Estimation of Driver Lane Change Intention Based on the LSTM and Dempster–Shafer Evidence Theory
2021
Journal of Advanced Transportation
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The outcome of this work is an essential component for all levels of road vehicle automation. ...
The experimental results show that the accuracy of the model is 90.7% for the intention of changing left and 89.1% for the intention of changing right. ...
Acknowledgments is research was supported by National Natural Science Foundation of China (61403172). e authors also wish to thank all the participants, school administrators, and the local government ...
doi:10.1155/2021/8858902
fatcat:izptwacvyfaktb7dyyo5kmbq2m
Comprehensive study of driver behavior monitoring systems using computer vision and machine learning techniques
2024
Journal of Big Data
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An artificial intelligence (AI) system under consideration alerts drivers about potentially unsafe behaviors using real-time voice notifications. ...
This paper offers an all-embracing survey of neural network-based methodologies for studying these driver bio-metrics, presenting an exhaustive examination of their advantages and drawbacks. ...
Acknowledgements We would like to thank the anonymous reviewers for their constructive feedback and inspiring comments. The reviewers invaluable comments eminently improved this survey paper. ...
doi:10.1186/s40537-024-00890-0
fatcat:oxt3rap74bhobdthnntf45xdde
A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information
2022
Journal of Advanced Transportation
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An accurate and reliable method of driving behavior recognition is of great significance and guidance for vehicle driving safety. ...
Finally, this paper points out some content that needs to be further explored, to provide reference and inspiration for scholars in this field to continue to study the driving behavior recognition model ...
At the same time, the rapid development of big data technology and artificial intelligence technology provides technical support for the research of driving behavior recognition models. ...
doi:10.1155/2022/7287511
fatcat:tavmgclbm5he3mkdjvtrjvatxm
A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning
2020
Sensors
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In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. ...
The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at ...
The input of the Bi-LSTM behavior recognition model is , where: , Here, , , . ...
doi:10.3390/s20174887
pmid:32872356
pmcid:PMC7506877
fatcat:qejeg3tmnvbdvlhqtvlrse2nbe
Driver Lane Change Intention Recognition Based on Attention Enhanced Residual-MBi-LSTM Network
2022
IEEE Access
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) model is proposed for lane change intention recognition in this paper. ...
Finally, the vehicle lane-changing intention recognition model is firstly trained and then verified in the HighD dataset. ...
Based on the trajectory characteristics and vehicle interaction information, this paper proposes an attention-enhanced residual MBi-LSTM model for driver lane change intention recognition. ...
doi:10.1109/access.2022.3179007
fatcat:ut24jj33jzah5eogkpjqu3iota
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs
2018
2018 IEEE Intelligent Vehicles Symposium (IV)
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2018 pre-print arXiv:1805.05499v1 fatcat:73jzv42vgzhzbnt2orlts2rv3u
In this paper, we present an LSTM model for interaction aware motion prediction of surrounding vehicles on freeways. ...
Our results show an improvement in terms of RMS values of prediction error. ...
in trajectory prediction with improved maneuver recognition Fig 5 shows the RMS values of prediction error for the 4 system settings considered. ...
doi:10.1109/ivs.2018.8500493
dblp:conf/ivs/DeoT18
fatcat:i5biixdw7faxvpqtin43filnim
Bidirectional Long Short-Term Memory Network for Vehicle Behavior Recognition
2018
Remote Sensing
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Vehicle behavior recognition is an attractive research field which is useful for many computer vision and intelligent traffic analysis tasks. ...
This paper presents an all-in-one behavior recognition framework for moving vehicles based on the latest deep learning techniques. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs10060887
fatcat:dzm5lrxcmnae5p2d5t4r3umfjy
Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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The dataset includes 104 hours of real human driving in the San Francisco Bay Area collected using an instrumented vehicle equipped with different sensors. ...
Driving Scene understanding is a key ingredient for intelligent transportation systems. ...
Long-Short Term Memory (LSTM) networks were shown to be successful in many temporal modeling tasks, including activity detection. We thus employ an LSTM as the backbone architecture for our model. ...
doi:10.1109/cvpr.2018.00803
dblp:conf/cvpr/RamanishkaCMS18
fatcat:fry4tklws5agho6kk2gl5ynhym
IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation
2020
IEEE Access
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In the article ''An improved LSTM model for behavior recognition of intelligent vehicles,'' by Xiao et al., the authors applied LSTM to the vehicle behavior recognition problem to identify the left turn ...
In the article ''Object recognition based interpolation with 3D LIDAR and vision for autonomous driving of an intelligent vehicle,'' by Weon, et al., the authors proposed an algorithm for fusing 3-D LIDAR ...
doi:10.1109/access.2020.3035440
fatcat:r3i3wkhttndbjnyciux3xuny3a
Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning
[article]
2018
arXiv
pre-print
Preserved Fulltext
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The dataset includes 104 hours of real human driving in the San Francisco Bay Area collected using an instrumented vehicle equipped with different sensors. ...
Driving Scene understanding is a key ingredient for intelligent transportation systems. ...
We thus employ an LSTM as the backbone architecture for our model. ...
arXiv:1811.02307v1
fatcat:q6nckxa5orfobeu24gabrtsdr4
Roadside pedestrian motion prediction using Bayesian methods and particle filter
2021
IET Intelligent Transport Systems
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Accidents between vehicles and pedestrians account for a large partition of severe traffic accidents. So, pedestrian motion prediction becomes a major concern for intelligent vehicles. ...
The results show that this method can give an accurate distribution of pedestrians' future trajectories. ...
Research Center for Intelligent Mobility (20193910045). ...
doi:10.1049/itr2.12090
fatcat:ikfhrdnwrva4lgxnsoplwqpjfe
Coordinated Decision Control of Lane-Change and Car-Following for Intelligent Vehicle Based on Time Series Prediction and Deep Reinforcement Learning
2024
Sensors
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LSTM network is introduced to predict the driving states of surrounding vehicles in multi-step time series, combining D3QN algorithm to make decisions on lane-change behavior. ...
To enhance the operational efficiency of intelligent vehicles in combined lane-change and car-following scenarios, we propose a coordinated decision control model based on hierarchical time series prediction ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s24020403
pmid:38257495
pmcid:PMC10818905
fatcat:5sqxcthhr5bjvdsaakaeo5o7wm
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