Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

Wang, Pangwei; Yu, Hongsheng; Liu, Cheng; Wang, Yunfeng; Ye, Rongsheng

doi:10.3390/s23062950

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…It has achieved good prediction accuracy and good performance improvement. In [28], this paper proposed a real-time trajectory prediction method for ICV based on vehicle to object (V2X) communication, which considers more dynamic spatial environments and improves the accuracy of trajectory prediction. Ref.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Intersections Prediction Based on Markov Model with Variable Weight Optimization

Ning

Jiang

et al. 2023

Sustainability

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In this study, a new algorithm for predicting vehicle turning at intersections is proposed. The method is based on the Markov chain and can predict vehicle trajectories using GPS location sequences. Unlike traditional Markov models, which use preset weights, we created the Markov model using a data-driven weight selection method. The proposed model can dynamically adjust the weights of each intersection’s influence on current trajectories based on the data, in contrast to the fixed weights in traditional models. The study also details how to process trajectory data to identify whether a vehicle has passed through an intersection and how to determine the adjacency relationship of intersections, thus providing a reference for implementing a model of the classification problem. The data-driven algorithm was applied and compared to the fixed-weight algorithm on the same trajectory dataset, and the superiority of the weight selection algorithm was proven. The prediction accuracy of the traditional method was 49.61%, while the proposed method achieved a prediction accuracy of 60.66% for 100,000 trajectory datasets, nearly an 11% increase. Volunteer participation in the second dataset collected on the university campus showed that the accuracy of the proposed method could be further improved to 79.31% as the GPS sampling frequency increased. Simulation results show that the algorithm provides accurate prediction and that the prediction effect is improved with the expansion of the trajectory data set and the increase in GPS sampling frequency. The proposed algorithm has the potential to provide a location-based optimization of network resource allocation.

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Section: Introductionmentioning

confidence: 99%

Vehicle Intersections Prediction Based on Markov Model with Variable Weight Optimization

Ning

Jiang

et al. 2023

Sustainability

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“…The long short-term memory (LSTM) model is one of the research paradigms of deep learning that has been recently used for traffic prediction in ITS [4], [5] [6], [7]. It has the inherent capability of modeling the stochastic nature of traffic data and identifying their spatio-temporal characteristics.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends

Khan,

Fouda,

et al. 2023

IEEE Access

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This paper surveys the short-term road traffic forecast algorithms based on the long-short term memory (LSTM) model of deep learning. The algorithms developed in the last three years are studied and analyzed. This provides an in-depth and thorough description of the algorithms rather than their marginal description as performed in the existing surveys that focus on general deep learning algorithms. The chosen algorithms are classified depending upon the use of LSTM in combination with other techniques for processing input data features towards a final traffic forecast. The operational strategies of the algorithms are described with merits and limitations. Moreover, a comparative analysis of the compared classes of algorithms is also provided. These strategies are helpful in selection of the right algorithms and their classes for the diverse traffic conditions and their future investigation for improvement. Besides, the applications of these classes of algorithms to traffic forecast in various networks for the latest decade is graphically depicted. Moreover, the applications of the LSTM in other fields involving a forecast are provided. Finally, the challenges associated with the short-term traffic forecast using the LSTM are described and strategies are highlighted for their future investigation.INDEX TERMS short-term traffic prediction, long short-term memory, LSTM, deep learning, intelligent transportation.

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State recognition and temperature rise time prediction of tobacco curing using multi-sensor data-fusion method based on feature impact factor

Pei,

Zhou,

Huang

et al. 2024

Expert Systems with Applications

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Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

Cited by 5 publications

References 34 publications

Vehicle Intersections Prediction Based on Markov Model with Variable Weight Optimization

Vehicle Intersections Prediction Based on Markov Model with Variable Weight Optimization

Short-Term Traffic Prediction Using Deep Learning Long Short-Term Memory: Taxonomy, Applications, Challenges, and Future Trends

State recognition and temperature rise time prediction of tobacco curing using multi-sensor data-fusion method based on feature impact factor

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