Cargando…
Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization
The intelligent transportation system, especially autonomous vehicles, has seen a lot of interest among researchers owing to the tremendous work in modern artificial intelligence (AI) techniques, especially deep neural learning. As a result of increased road accidents over the last few decades, sign...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785663/ https://www.ncbi.nlm.nih.gov/pubmed/36560047 http://dx.doi.org/10.3390/s22249677 |
_version_ | 1784858103442833408 |
---|---|
author | Kolekar, Suresh Gite, Shilpa Pradhan, Biswajeet Alamri, Abdullah |
author_facet | Kolekar, Suresh Gite, Shilpa Pradhan, Biswajeet Alamri, Abdullah |
author_sort | Kolekar, Suresh |
collection | PubMed |
description | The intelligent transportation system, especially autonomous vehicles, has seen a lot of interest among researchers owing to the tremendous work in modern artificial intelligence (AI) techniques, especially deep neural learning. As a result of increased road accidents over the last few decades, significant industries are moving to design and develop autonomous vehicles. Understanding the surrounding environment is essential for understanding the behavior of nearby vehicles to enable the safe navigation of autonomous vehicles in crowded traffic environments. Several datasets are available for autonomous vehicles focusing only on structured driving environments. To develop an intelligent vehicle that drives in real-world traffic environments, which are unstructured by nature, there should be an availability of a dataset for an autonomous vehicle that focuses on unstructured traffic environments. Indian Driving Lite dataset (IDD-Lite), focused on an unstructured driving environment, was released as an online competition in NCPPRIPG 2019. This study proposed an explainable inception-based U-Net model with Grad-CAM visualization for semantic segmentation that combines an inception-based module as an encoder for automatic extraction of features and passes to a decoder for the reconstruction of the segmentation feature map. The black-box nature of deep neural networks failed to build trust within consumers. Grad-CAM is used to interpret the deep-learning-based inception U-Net model to increase consumer trust. The proposed inception U-net with Grad-CAM model achieves 0.622 intersection over union (IoU) on the Indian Driving Dataset (IDD-Lite), outperforming the state-of-the-art (SOTA) deep neural-network-based segmentation models. |
format | Online Article Text |
id | pubmed-9785663 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97856632022-12-24 Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization Kolekar, Suresh Gite, Shilpa Pradhan, Biswajeet Alamri, Abdullah Sensors (Basel) Article The intelligent transportation system, especially autonomous vehicles, has seen a lot of interest among researchers owing to the tremendous work in modern artificial intelligence (AI) techniques, especially deep neural learning. As a result of increased road accidents over the last few decades, significant industries are moving to design and develop autonomous vehicles. Understanding the surrounding environment is essential for understanding the behavior of nearby vehicles to enable the safe navigation of autonomous vehicles in crowded traffic environments. Several datasets are available for autonomous vehicles focusing only on structured driving environments. To develop an intelligent vehicle that drives in real-world traffic environments, which are unstructured by nature, there should be an availability of a dataset for an autonomous vehicle that focuses on unstructured traffic environments. Indian Driving Lite dataset (IDD-Lite), focused on an unstructured driving environment, was released as an online competition in NCPPRIPG 2019. This study proposed an explainable inception-based U-Net model with Grad-CAM visualization for semantic segmentation that combines an inception-based module as an encoder for automatic extraction of features and passes to a decoder for the reconstruction of the segmentation feature map. The black-box nature of deep neural networks failed to build trust within consumers. Grad-CAM is used to interpret the deep-learning-based inception U-Net model to increase consumer trust. The proposed inception U-net with Grad-CAM model achieves 0.622 intersection over union (IoU) on the Indian Driving Dataset (IDD-Lite), outperforming the state-of-the-art (SOTA) deep neural-network-based segmentation models. MDPI 2022-12-10 /pmc/articles/PMC9785663/ /pubmed/36560047 http://dx.doi.org/10.3390/s22249677 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kolekar, Suresh Gite, Shilpa Pradhan, Biswajeet Alamri, Abdullah Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization |
title | Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization |
title_full | Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization |
title_fullStr | Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization |
title_full_unstemmed | Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization |
title_short | Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization |
title_sort | explainable ai in scene understanding for autonomous vehicles in unstructured traffic environments on indian roads using the inception u-net model with grad-cam visualization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785663/ https://www.ncbi.nlm.nih.gov/pubmed/36560047 http://dx.doi.org/10.3390/s22249677 |
work_keys_str_mv | AT kolekarsuresh explainableaiinsceneunderstandingforautonomousvehiclesinunstructuredtrafficenvironmentsonindianroadsusingtheinceptionunetmodelwithgradcamvisualization AT giteshilpa explainableaiinsceneunderstandingforautonomousvehiclesinunstructuredtrafficenvironmentsonindianroadsusingtheinceptionunetmodelwithgradcamvisualization AT pradhanbiswajeet explainableaiinsceneunderstandingforautonomousvehiclesinunstructuredtrafficenvironmentsonindianroadsusingtheinceptionunetmodelwithgradcamvisualization AT alamriabdullah explainableaiinsceneunderstandingforautonomousvehiclesinunstructuredtrafficenvironmentsonindianroadsusingtheinceptionunetmodelwithgradcamvisualization |