A Novel Virtual Navigation Route Generation Scheme for Augmented Reality Car Navigation System

This paper develops a novel virtual navigation route generation scheme for an augmented reality (AR) car navigation system based on the generative adversarial network–long short-term memory network (GAN–LSTM) framework with an integrated camera and GPS module. Unlike the present AR car navigation sy...

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Published inSensors (Basel, Switzerland) Vol. 25; no. 3; p. 820
Main Authors Lin, Yu-Chen, Chan, Yu-Ching, Lin, Ming-Chih
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 30.01.2025
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Abstract This paper develops a novel virtual navigation route generation scheme for an augmented reality (AR) car navigation system based on the generative adversarial network–long short-term memory network (GAN–LSTM) framework with an integrated camera and GPS module. Unlike the present AR car navigation systems, the virtual navigation route is “autonomously” generated in captured images rather than superimposed on the image utilizing the pre-rendered 3D content, such as an arrow or trajectory, which not only provide a more authentic and correct AR effect to the user but also correctly guide the driver earlier when driving in complex road traffic environments. First, an evolved fully convolutional network architecture which uses a top-view image through an inverse perspective mapping scheme as input is utilized to obtain a more accurate semantic segmentation result for the lane markings in the traffic scene. Next, according to the above segmentation result and known location information from path planning, an AR Navigation-Nets based on an LSTM framework is proposed to predict the global relationship codes of the virtual navigation route. Simultaneously, the discriminator is utilized to evaluate the generated virtual navigation route that can approximate the real-world vehicle trajectory. Finally, the virtual navigation route can be superimposed on the original image with the correct ratio and position through an IPM process.
AbstractList This paper develops a novel virtual navigation route generation scheme for an augmented reality (AR) car navigation system based on the generative adversarial network-long short-term memory network (GAN-LSTM) framework with an integrated camera and GPS module. Unlike the present AR car navigation systems, the virtual navigation route is "autonomously" generated in captured images rather than superimposed on the image utilizing the pre-rendered 3D content, such as an arrow or trajectory, which not only provide a more authentic and correct AR effect to the user but also correctly guide the driver earlier when driving in complex road traffic environments. First, an evolved fully convolutional network architecture which uses a top-view image through an inverse perspective mapping scheme as input is utilized to obtain a more accurate semantic segmentation result for the lane markings in the traffic scene. Next, according to the above segmentation result and known location information from path planning, an AR Navigation-Nets based on an LSTM framework is proposed to predict the global relationship codes of the virtual navigation route. Simultaneously, the discriminator is utilized to evaluate the generated virtual navigation route that can approximate the real-world vehicle trajectory. Finally, the virtual navigation route can be superimposed on the original image with the correct ratio and position through an IPM process.This paper develops a novel virtual navigation route generation scheme for an augmented reality (AR) car navigation system based on the generative adversarial network-long short-term memory network (GAN-LSTM) framework with an integrated camera and GPS module. Unlike the present AR car navigation systems, the virtual navigation route is "autonomously" generated in captured images rather than superimposed on the image utilizing the pre-rendered 3D content, such as an arrow or trajectory, which not only provide a more authentic and correct AR effect to the user but also correctly guide the driver earlier when driving in complex road traffic environments. First, an evolved fully convolutional network architecture which uses a top-view image through an inverse perspective mapping scheme as input is utilized to obtain a more accurate semantic segmentation result for the lane markings in the traffic scene. Next, according to the above segmentation result and known location information from path planning, an AR Navigation-Nets based on an LSTM framework is proposed to predict the global relationship codes of the virtual navigation route. Simultaneously, the discriminator is utilized to evaluate the generated virtual navigation route that can approximate the real-world vehicle trajectory. Finally, the virtual navigation route can be superimposed on the original image with the correct ratio and position through an IPM process.
This paper develops a novel virtual navigation route generation scheme for an augmented reality (AR) car navigation system based on the generative adversarial network–long short-term memory network (GAN–LSTM) framework with an integrated camera and GPS module. Unlike the present AR car navigation systems, the virtual navigation route is “autonomously” generated in captured images rather than superimposed on the image utilizing the pre-rendered 3D content, such as an arrow or trajectory, which not only provide a more authentic and correct AR effect to the user but also correctly guide the driver earlier when driving in complex road traffic environments. First, an evolved fully convolutional network architecture which uses a top-view image through an inverse perspective mapping scheme as input is utilized to obtain a more accurate semantic segmentation result for the lane markings in the traffic scene. Next, according to the above segmentation result and known location information from path planning, an AR Navigation-Nets based on an LSTM framework is proposed to predict the global relationship codes of the virtual navigation route. Simultaneously, the discriminator is utilized to evaluate the generated virtual navigation route that can approximate the real-world vehicle trajectory. Finally, the virtual navigation route can be superimposed on the original image with the correct ratio and position through an IPM process.
Audience Academic
Author Lin, Ming-Chih
Chan, Yu-Ching
Lin, Yu-Chen
AuthorAffiliation Department of Automatic Control Engineering, Feng Chia University, Taichung City 407102, Taiwan; james397520@gmail.com (Y.-C.C.); mingchih071126@gmail.com (M.-C.L.)
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virtual navigation route
augmented reality
long short-term memory network
semantic segmentation
navigation system
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Snippet This paper develops a novel virtual navigation route generation scheme for an augmented reality (AR) car navigation system based on the generative adversarial...
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StartPage 820
SubjectTerms Augmented Reality
Cellular telephones
generative adversarial network
Liquors
long short-term memory network
navigation system
Navigation systems
Roads & highways
semantic segmentation
Telematics
Three dimensional imaging
virtual navigation route
Virtual reality
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Title A Novel Virtual Navigation Route Generation Scheme for Augmented Reality Car Navigation System
URI https://www.ncbi.nlm.nih.gov/pubmed/39943459
https://www.proquest.com/docview/3165920546
https://www.proquest.com/docview/3166266893
https://pubmed.ncbi.nlm.nih.gov/PMC11819711
https://doaj.org/article/14119c2f27c542399a15fc466d11c8a5
Volume 25
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