Artificial Intelligence (AI) in the automotive industry allows car manufacturers to produce intelligent and autonomous vehicles through the integration of AI-powered Advanced Driver Assistance Systems (ADAS) and/or Automated Driving Systems (ADS) such as the Traffic Sign Recognition (TSR) system. Existing TSR solutions focus on some categories of signs they recognise. For this reason, a TSR approach encompassing more road sign categories like Warning, Regulatory, Obligatory, and Priority signs is proposed to build an intelligent and real-time system able to analyse, detect, and classify traffic signs into their correct categories. The proposed approach is based on an overview of different Traffic Sign Detection (TSD) and Traffic Sign Classification (TSC) methods, aiming to choose the best ones in terms of accuracy and processing time. Hence, the proposed methodology combines the Haar cascade technique with a deep CNN model classifier. The developed TSC model is trained on the GTSRB dataset and then tested on various categories of road signs. The achieved testing accuracy rate reaches 98.56%. In order to improve the classification performance, we propose a new attention-based deep convolutional neural network. The achieved results are better than those existing in other traffic sign classification studies since the obtained testing accuracy and F1-measure rates achieve, respectively, 99.91% and 99%. The developed TSR system is evaluated and validated on a Raspberry Pi 4 board. Experimental results confirm the reliable performance of the suggested approach.

The continuous evolution of autonomous vehicles technologies has significantly elevated the capabilities of intelligent transportation and road safety. Among these advancements, driving automation systems have played a vital role within vehicles, encompassing a diversity of functionality extending from systems assisting the driver called Advanced Driver Assistance Systems to Automated Driving Systems offering full control over various driving functions. Among these systems, Traffic Sign Recognition (TSR) system plays a significant role in terms of functionality and development. This survey focuses on the development of TSR system including both detection and classification methods considering diverse techniques based on color, shape, machine learning and deep learning algorithms and offers insights into the various approaches. Hence a comparative synthesis is established to discuss thorough of them along. Furthermore, this paper presents a comprehensive study for TSR systems using two hardware platforms: Raspberry Pi and Nano Jetson to analyze their performances and provide insights into the most suitable hardware architectures for deploying efficient and reliable TSR systems. This paper concludes by advocating for a synergistic combination of the two best methods of detection and classification of road signs in terms of accuracy and processing time to build a new real time TSR methodology that effectively address this challenge and ultimately improve TSR performance implemented on the best board.

Advanced Driver Assistance Systems (ADAS) technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road. Traffic Sign Recognition System (TSRS) is one of the most important components of ADAS. Among the challenges with TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time. Accordingly, this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules. Firstly, the Speed Limit Detection (SLD) module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames. Secondly, the Speed Limit Classification (SLC) module, featuring machine learning classifiers alongside a newly developed model called DeepSL, harnesses the power of a CNN architecture to extract intricate features from speed limit sign images, ensuring efficient and precise recognition. In addition, a new Speed Limit Classifiers Fusion (SLCF) module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique. Through rigorous software and hardware validation processes, the proposed methodology has achieved highly significant F1 scores of 99.98% and 99.96% for DS theory and the voting method, respectively. Furthermore, a prototype encompassing all components demonstrates outstanding reliability and efficacy, with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board, marking a significant advancement in TSRS technology.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) hold significant potential for enhancing driver safety through real-time monitoring of cognitive and affective states. However, the development of reliable BCI systems for Advanced Driver Assistance Systems (ADAS) depends on the availability of high-quality, publicly accessible EEG datasets collected during driving tasks. Existing datasets lack standardized parameters and contain demographic biases, which undermine their reliability and prevent the development of robust systems. This study presents a multidimensional benchmark analysis of seven publicly available EEG driving datasets. We compare these datasets across multiple dimensions, including task design, modality integration, demographic representation, accessibility, and reported model performance. This benchmark synthesizes existing literature without conducting new experiments. Our analysis reveals critical gaps, including significant age and gender biases, overreliance on simulated environments, insufficient affective monitoring, and restricted data accessibility. These limitations hinder real-world applicability and reduce ADAS performance. To address these gaps and facilitate the development of generalizable BCI systems, this study provides a structured, quantitative benchmark analysis of publicly available driving EEG datasets, suggesting criteria and recommendations for future dataset design and use. Additionally, we emphasize the need for balanced participant distributions, standardized emotional annotation, and open data practices.