Traffic Sign Detection for Intelligent Transportation Systems: A Survey

Ellahyani, Ayoub; Jaafari, Ilyas El; Charfi, Said

doi:10.1051/e3sconf/202122901006

Cited by 24 publications

(6 citation statements)

References 62 publications

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“…Integrating real-time image processing with driver assistance systems can improve OSH in driving scenarios. ( 72 ). Accordingly, cognitive load can be reduced, and overall driving safety can be improved ( 73 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

Teng

Wan

Kong

et al. 2023

Front. Public Health

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MotivationAugmented reality head-up display (AR-HUD) interface design takes on critical significance in enhancing driving safety and user experience among professional drivers. However, optimizing the above-mentioned interfaces poses challenges, innovative methods are urgently required to enhance performance and reduce cognitive load.DescriptionA novel method was proposed, combining the IVPM method with a GA to optimize AR-HUD interfaces. Leveraging machine learning, the IVPM-GA method was adopted to predict cognitive load and iteratively optimize the interface design.ResultsExperimental results confirmed the superiority of IVPM-GA over the conventional BP-GA method. Optimized AR-HUD interfaces using IVPM-GA significantly enhanced the driving performance, and user experience was enhanced since 80% of participants rated the IVPM-GA interface as visually comfortable and less distracting.ConclusionIn this study, an innovative method was presented to optimize AR-HUD interfaces by integrating IVPM with a GA. IVPM-GA effectively reduced cognitive load, enhanced driving performance, and improved user experience for professional drivers. The above-described findings stress the significance of using machine learning and optimization techniques in AR-HUD interface design, with the aim of enhancing driver safety and occupational health. The study confirmed the practical implications of machine learning optimization algorithms for designing AR-HUD interfaces with reduced cognitive load and improved occupational safety and health (OSH) for professional drivers.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

Teng

Wan

Kong

et al. 2023

Front. Public Health

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“…For making auto-working and smart vehicles lot of efforts are already made in form of convolution network trained models with different proposed methodologies for detection and recognition. A recent approach [6] having wide applications for traffic sign detecting and recognizing intelligent transportation systems that informs driver about precaution measures and sign related information, uses color, shape and ML algorithm-based methods, and provided comparative information on the same. Color space, segmentation method, features, and shape detection method are the terms considered in the review of the detection module.…”

Section: Literature Surveymentioning

confidence: 99%

“…Python language is very useful for building machine learning models, artificial intelligence supported applications and performing data analysis. This interpreted language evaluates the code fast, has easy-touse syntax and supports thousands of libraries for analysis of real-world scenarios, and therefore this work is based on Python programming [6]. The widely used python libraries of Numpy and Pandas for data manipulation have also been used.…”

Section: Language Platform and Librariesmentioning

confidence: 99%

CNN Based Approach for Traffic Sign Recognition System

Singh

Malik²

2021

Adv. J. Grad. Res.

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Machine Learning (ML) involves making a machine able to learn and take decisions on real-life problems by working with an efficient set of algorithms. The generated ML models find application in different areas of research and management. One such field, automotive technology, employs ML enabled commercialized advanced driver assistance systems (ADAS) which include traffic sign recognition as a part. With the increasing demand for the intelligence of vehicles, and the advent of self-driving cars, it is extremely necessary to detect and recognize traffic signs automatically through computer technology. For this, neural networks can be applied for analyzing images of traffic signs for cognitive decision making by autonomous vehicles. Neural networks are the computing systems which act as a means of performing ML. In this work, a convolutional neural network (CNN) based ML model is built for recognition of traffic signs accurately for decision making, when installed in driverless vehicles.

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“…Traffic Sign Recognition (TSR) has emerged as a fundamental component of intelligent transportation systems, playing a crucial role in enhancing road safety and efficiency. With recent advancements in artificial intelligence and machine learning (AI&ML), TSR systems have become capable of automating the detection and classification of traffic signs, providing drivers with crucial real-time information [1,2]. This technological progress plays an important role in reducing accidents, optimizing traffic flow, and improving the safety standards on our roads.…”

Section: Introductionmentioning

confidence: 99%

Exploring Explainable Artificial Intelligence Techniques for Interpretable Neural Networks in Traffic Sign Recognition Systems

Khan,

Park

2024

Electronics

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Traffic Sign Recognition (TSR) plays a vital role in intelligent transportation systems (ITS) to improve road safety and optimize traffic management. While existing TSR models perform well in challenging scenarios, their lack of transparency and interpretability hinders reliability, trustworthiness, validation, and bias identification. To address this issue, we propose a Convolutional Neural Network (CNN)-based model for TSR and evaluate its performance on three benchmark datasets: German Traffic Sign Recognition Benchmark (GTSRB), Indian Traffic Sign Dataset (ITSD), and Belgian Traffic Sign Dataset (BTSD). The proposed model achieves an accuracy of 98.85% on GTSRB, 94.73% on ITSD, and 92.69% on BTSD, outperforming several state-of-the-art frameworks, such as VGG19, VGG16, ResNet50V2, MobileNetV2, DenseNet121, DenseNet201, NASNetMobile, and EfficientNet, while also providing faster training and response times. We further enhance our model by incorporating explainable AI (XAI) techniques, specifically, Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM), providing clear insights of the proposed model decision-making process. This integration allows the extension of our TSR model to various engineering domains, including autonomous vehicles, advanced driver assistance systems (ADAS), and smart traffic control systems. The practical implementation of our model ensures real-time, accurate recognition of traffic signs, thus optimizing traffic flow and minimizing accident risks.

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Traffic Sign Detection for Intelligent Transportation Systems: A Survey

Cited by 24 publications

References 62 publications

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers

CNN Based Approach for Traffic Sign Recognition System

Exploring Explainable Artificial Intelligence Techniques for Interpretable Neural Networks in Traffic Sign Recognition Systems

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