Target vehicle lane-change intention detection: An approach based on online transfer learning

2021 ◽  
Author(s):  
Hailun Zhang ◽  
Rui Fu
Keyword(s):  
Transfer Learning ◽  
Lane Change ◽  
Target Vehicle

scholarly journals Target Vehicle Selection Algorithm for Adaptive Cruise Control Based on Lane-changing Intention of Preceding Vehicle

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Jun Yao ◽  
Guoying Chen ◽  
Zhenhai Gao
Keyword(s):  
Adaptive Cruise Control ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Lane Change ◽  
Selection Algorithm ◽  
Cruise Control ◽  
Lane Changing ◽  
Target Vehicle ◽  
Preceding Vehicle ◽  
The Subject

AbstractTo improve the ride comfort and safety of a traditional adaptive cruise control (ACC) system when the preceding vehicle changes lanes, it proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention for the preceding vehicle. First, the Next Generation Simulation dataset is used to train a lane-changing intention prediction algorithm based on a sliding window support vector machine, and the lane-changing intention of the preceding vehicle in the current lane is identified by lateral position offset. Second, according to the lane-changing intention and collision threat of the preceding vehicle, the target vehicle selection algorithm is studied under three different conditions: safe lane-changing, dangerous lane-changing, and lane-changing cancellation. Finally, the effectiveness of the proposed algorithm is verified in a co–simulation platform. The simulation results show that the target vehicle selection algorithm can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels their lane change maneuver. In the case of a dangerous lane change, the target vehicle selection algorithm proposed in this paper can respond more rapidly to a dangerous lane change than the target vehicle selection method of the traditional ACC system; thus, it can effectively avoid collisions and improve the safety of the subject vehicle.

scholarly journals Target Vehicle Selection Algorithm Based on Lane-changing Intention of Preceding Vehicle for ACC

2021 ◽  
Author(s):  
Jun Yao ◽  
Guoying Chen ◽  
Zhenhai Gao
Keyword(s):  
Ride Comfort ◽  
Lateral Position ◽  
Prediction Algorithm ◽  
Lane Change ◽  
Selection Algorithm ◽  
Cruise Control ◽  
Lane Changing ◽  
Target Vehicle ◽  
Preceding Vehicle ◽  
The Subject

Abstract In order to improve the ride comfort and safety of the traditional adaptive cruise control (ACC) system when the preceding vehicle changes lanes, this paper proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention of the preceding vehicle. First, NGSIM dataset is used to train a lane-changing intention prediction algorithm based on sliding window SVM, and the lane-changing intent of the preceding vehicle in the current lane can be identified by lateral position offset. Secondly, according to the lane-changing intention and the collision threat of the preceding vehicle, the target vehicle selection algorithm is studied under three different conditions: safe lane-changing condition, dangerous lane-changing condition, and lane-changing cancellation condition. Finally, the effectiveness of the algorithm proposed in this paper is verified in the co-simulation platform. The simulation results show that the target vehicle selection algorithm proposed in this paper can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels the lane change. In the case of a dangerous lane change, the target vehicle selection algorithm proposed in this paper can respond to the dangerous lane change in advance compared with the target vehicle selection method of the traditional ACC system, which can effectively avoid collisions and improve the safety of the subject vehicle.

scholarly journals Target Vehicle Selection Algorithm Based on Lane-changing Intention of Preceding Vehicle for ACC

2020 ◽  
Author(s):  
Jun Yao ◽  
Guoying Chen ◽  
Zhenhai Gao
Keyword(s):  
Ride Comfort ◽  
Lateral Position ◽  
Prediction Algorithm ◽  
Lane Change ◽  
Selection Algorithm ◽  
Cruise Control ◽  
Lane Changing ◽  
Target Vehicle ◽  
Preceding Vehicle ◽  
The Subject

Abstract In order to improve the ride comfort and safety of the traditional adaptive cruise control (ACC) system when the preceding vehicle changes lanes, this paper proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention of the preceding vehicle. First, NGSIM dataset is used to train a lane-changing intention prediction algorithm based on sliding window SVM, and the lane-changing intent of the preceding vehicle in the current lane can be identified by lateral position offset. Secondly, according to the lane-changing intention and the collision threat of the preceding vehicle, the target vehicle selection algorithm is studied under three different conditions: safe lane-changing condition, dangerous lane-changing condition, and lane-changing cancellation condition. Finally, the effectiveness of the algorithm proposed in this paper is verified in the co-simulation platform. The simulation results show that the target vehicle selection algorithm proposed in this paper can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels the lane change. In the case of a dangerous lane change, the target vehicle selection algorithm proposed in this paper can respond to the dangerous lane change in advance compared with the target vehicle selection method of the traditional ACC system, which can effectively avoid collisions and improve the safety of the subject vehicle.

292. Target Vehicle Health Hazard Assessment

1999 ◽  
Author(s):  
J. Soyak ◽  
P. Crawford ◽  
J. Gaughan ◽  
J. Mazur
Keyword(s):  
Hazard Assessment ◽  
Health Hazard ◽  
Target Vehicle

Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

2019 ◽  
Author(s):  
Qi Yuan ◽  
Alejandro Santana-Bonilla ◽  
Martijn Zwijnenburg ◽  
Kim Jelfs
Keyword(s):  
Neural Network ◽  
Electronic Properties ◽  
Transfer Learning ◽  
Recurrent Neural Network ◽  
Chemical Space ◽  
Generative Models ◽  
Molecular Features ◽  
Donor Acceptor ◽  
Homo Lumo ◽  
Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning

2014 ◽  
Author(s):  
Hiroshi Kanayama ◽  
Youngja Park ◽  
Yuta Tsuboi ◽  
Dongmook Yi
Keyword(s):  
Transfer Learning

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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