Lane Change Trajectory Prediction based on Spatiotemporal Attention Mechanism
Author:
Affiliation:
1. School of Transportation Science Engineering at Beihang University
2. School of Transportation Science and Engineering, Beihang University
3. Research Institute for Frontier Science, Beihang University
4. Beihang University
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9921415/9921739/09922250.pdf?arnumber=9922250
Reference25 articles.
1. A survey of deep learning techniques for autonomous driving
2. Evolutionary multi-objective generation of recurrent neural network ensembles for time series prediction
3. Structural-RNN: Deep Learning on Spatio-Temporal Graphs
4. Capturing Drivers’ Lane Changing Behaviors on Operational Level by Data Driven Methods
5. Surround vehicles trajectory analysis with recurrent neural networks
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1. A Comprehensive Investigation of Lane-Changing Risk Recognition Framework of Multi-Vehicle Type Considering Key Features Based on Vehicles’ Trajectory Data;Electronics;2024-03-16
2. Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions;Vehicular Communications;2024-01
3. MEC-Enabled Lane Change Prediction with Spatiotemporal Attention Mechanism for ITS;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24
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