A CNN-LSTM Based Model to Predict Trajectory of Human-Driven Vehicle-Reference-Cited by-同舟云学术

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A CNN-LSTM Based Model to Predict Trajectory of Human-Driven Vehicle

Published:2023-10-01 Issue: Volume:2022-Decem Page:3097-3103
ISSN:
Container-title:2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
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Author:

Alsanwy Shehab¹,Asadi Houshyar¹,Qazani Mohammad Reza Chalak²,Mohamed Shady¹,Nahavandi Saeid³

Affiliation:

1. Institute of Intelligent Systems Research and Innovation (IISRI), Deakin University,Geelong,VIC,Australia,3126

2. Murray State University,Department of Computer Science and Information Systems (CSIS),Murray,KY,USA,42071

3. Swinburne University of Technology,Hawthorn,VIC,Australia,3122

Funder

Australian Research Council (ARC)

Deakin University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10391856/10393862/10394243.pdf?arnumber=10394243

Reference68 articles.

1. Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks

2. Inferring the Driver’s Lane Change Intention through LiDAR-Based Environment Analysis Using Convolutional Neural Networks

3. Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information

4. Long-Term Prediction of Vehicle Trajectory Using Recurrent Neural Networks

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1. Vehicle Trajectory Prediction Using Deep Learning for Advanced Driver Assistance Systems and Autonomous Vehicles;2024 IEEE International Systems Conference (SysCon);2024-04-15

2. Optimum signal duration for Human Activity Recognition based on Deep Convolutional Neural Networks;2024 IEEE International Systems Conference (SysCon);2024-04-15

3. Objective Assessment of Cybersickness in a Stationary Helicopter Simulator Using Pupil Diameter as a Quantitative Indicator;2024 IEEE International Systems Conference (SysCon);2024-04-15

4. Application of a BiLSTM Model for Detecting Driver Distraction Caused by Hand-Held Mobile Phones, Utilizing Physiological Signals and Head Motion Data;2024 IEEE International Systems Conference (SysCon);2024-04-15

5. Galvanic Skin Response and AE-LSTM for Anomaly Detection in VR-Induced Motion Sickness;2024 IEEE International Systems Conference (SysCon);2024-04-15

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