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Using Deep Neural Networks to Classify Symbolic Road Markings for Autonomous Vehicles

Suarez-Mash, Daniel; Ghani, Arfan; See, Chan H.; Keates, Simeon; Yu, Hongnian

Authors

Daniel Suarez-Mash

Arfan Ghani

Simeon Keates



Abstract

To make autonomous cars as safe as feasible for all road users, it is essential to interpret as many sources of trustworthy information as possible. There has been substantial research into interpreting objects such as traffic lights and pedestrian information, however, less attention has been paid to the Symbolic Road Markings (SRMs). SRMs are essential information that needs to be interpreted by autonomous vehicles, hence, this case study presents a comprehensive model primarily focused on classifying painted symbolic road markings by using a region of interest (ROI) detector and a deep convolutional neural network (DCNN). This two-stage model has been trained and tested using an extensive public dataset. The two-stage model investigated in this research includes SRM classification by using Hough lines where features were extracted and the CNN model was trained and tested. An ROI detector is presented that crops and segments the road lane to eliminate non-essential features of the image. The investigated model is robust, achieving up to 92.96 percent accuracy with 26.07 and 40.1 frames per second (FPS) using ROI scaled and raw images, respectively.

Journal Article Type Article
Acceptance Date May 16, 2022
Online Publication Date May 16, 2022
Publication Date 2022
Deposit Date May 16, 2022
Publicly Available Date May 17, 2022
Journal EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
Print ISSN 2410-0218
Publisher EAI: European Alliance for Innovation
Peer Reviewed Peer Reviewed
Volume 9
Issue 31
Article Number e2
DOI https://doi.org/10.4108/eetinis.v9i31.985
Keywords convolutional neural networks; symbol road marking; autonomous cars; intelligent systems; system design; embedded systems
Public URL http://researchrepository.napier.ac.uk/Output/2872270
Publisher URL https://publications.eai.eu/index.php/inis/article/view/985

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