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Applied Sciences
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Trends and Future Prospects in Intelligent Vehicles and Autonomous Driving
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Trends and Future Prospects in Intelligent Vehicles and Autonomous Driving

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 17775

Special Issue Editor

Prof. Dr. Klaus Bengler

E-Mail Website
Guest Editor
Chair of Ergonomics, School of Engineering and Design, Technical University of Munich, 85748 Garching, Germany
Interests: human factors in automated driving

Special Issue Information

Dear Colleagues,

As a promising form of intelligent transportation, highly automated and autonomous vehicles have shown the potential to revolutionize our transportation systems. Considering that the roles of human drivers and passengers is still of critical importance, the focus of our attention shifts from the sheer feasibility of automated driving (AD) to the different facets of human factors in this field.

To better understand this human–machine system and relevant requirements, different research has been done. We need to comprehensively consider human-related issues to achieve possible innovations and overcome potential human discomfort. The major goal of AD is to ensure a safe, pleasant and sustainable/efficient driving experience, for both passengers and other road users.

This Special Issue aims to showcase the ongoing developments, new projects, current achievements and future perspectives of automated driving, from a human-centric perspective. Researchers are welcome to contribute original research or review papers. The major topics of this Special Issue include, but are not limited to:

  • Comfort in automated driving;
  • Usability of automated driving HMIs;
  • eHMI;
  • Interaction with other road users;
  • Effects of automated driving on driver status;
  • Automated driving for professional drivers;
  • HMI design strategies.

Prof. Dr. Klaus Bengler
Guest Editor

Manuscript Submission Information

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Keywords

  • comfort in automated driving
  • usability of automated driving HMIs
  • eHMI
  • interaction with other road users
  • effects of automated driving on driver status
  • automated driving for professional drivers
  • HMI design strategies

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Published Papers (1 paper)

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Research

21 pages, 5885 KiB  
Article
A Real-Time Traffic Sign Recognition Method Using a New Attention-Based Deep Convolutional Neural Network for Smart Vehicles
by Nesrine Triki, Mohamed Karray and Mohamed Ksantini
Appl. Sci. 2023, 13(8), 4793; https://doi.org/10.3390/app13084793 - 11 Apr 2023
Cited by 45 | Viewed by 16926
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Trends and Future Prospects in Intelligent Vehicles and Autonomous Driving)
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