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Applied Sciences
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AI-Driven Automotive Advances: From Passenger Monitoring to Autonomous Navigation
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AI-Driven Automotive Advances: From Passenger Monitoring to Autonomous Navigation

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 2024) | Viewed by 2835

Special Issue Editors

Prof. Dr. Jose M. Celaya-Padilla

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Guest Editor
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico
Interests: computer vision; sensor networks; automotive hmi; artificial intelligence
Prof. Dr. Huizilopoztli Luna García

E-Mail Website
Guest Editor
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico
Interests: human technology-interaction; infotainment systems; data processing
Special Issues, Collections and Topics in MDPI journals
Dr. Hamurabi Gamboa-Rosales

E-Mail Website
Guest Editor
Zacatecan Council for Science, Technology and Innovation, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro, Zacatecas 98000, Mexico
Interests: data analysis; signal processing; artificial intelligence; bioinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Martínez Torteya

E-Mail Website
Guest Editor
Escuela de Ingeniería y Tecnologías, Universidad de Monterrey, San Pedro Garza García, NL 66238, Mexico
Interests: machine learning; biomarkers

Special Issue Information

Dear Colleagues,

The rapid rise of artificial intelligence (AI) technologies has left an indelible mark on almost every industry, and the automotive domain is no exception. This Special Issue delves deep into the heart of AI-driven transformations that are redefining our vehicular experiences. Spanning the realms of real-time passenger monitoring to the futuristic aspirations of autonomous navigation, the range of topics illuminates the breadth and depth of AI's impact.

This Special Issue will be dedicated to AI-driven automotive advances; subjects that will be discussed in this Special Issue will focus not only on modern methods, technologies, and cutting-edge innovations in the automotive industry and their applications, but also on new approaches for vehicle and human safety on the road.

Prof. Dr. Jose M. Celaya-Padilla
Prof. Dr. Huizilopoztli Luna García
Dr. Hamurabi Gamboa-Rosales
Dr. Antonio Martínez Torteya
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart safety
  • passenger monitoring
  • driver assistance
  • next-gen heuristics for connected cars
  • autonomous driving and applications of artificial intelligence
  • driver assistance systems
  • autonomous driving technologies
  • sensor fusion in passenger monitoring
  • predictive analytics in driver assistance
  • road safety
  • vehicle-to-everything (V2X) communication
  • advanced driver assistance systems (ADASs)
  • augmented reality (AR) and virtual reality (VR) in driver assistance
  • UX and experience in the automotive industry

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Published Papers (2 papers)

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Research

20 pages, 816 KiB  
Article
A Multimodal Recurrent Model for Driver Distraction Detection
by Marcel Ciesla and Gerald Ostermayer
Appl. Sci. 2024, 14(19), 8935; https://doi.org/10.3390/app14198935 - 4 Oct 2024
Viewed by 354
Abstract
Distracted driving is a significant threat to road safety, causing numerous accidents every year. Driver distraction detection systems offer a promising solution by alerting the driver to refocus on the primary driving task. Even with increasing vehicle automation, human drivers must remain alert, [...] Read more.
Distracted driving is a significant threat to road safety, causing numerous accidents every year. Driver distraction detection systems offer a promising solution by alerting the driver to refocus on the primary driving task. Even with increasing vehicle automation, human drivers must remain alert, especially in partially automated vehicles where they may need to take control in critical situations. In this work, an AI-based distraction detection model is developed that focuses on improving classification performance using a long short-term memory (LSTM) network. Unlike traditional approaches that evaluate individual frames independently, the LSTM network captures temporal dependencies across multiple time steps. In addition, this study investigated the integration of vehicle sensor data and an inertial measurement unit (IMU) to further improve detection accuracy. The results show that the recurrent LSTM network significantly improved the average F1 score from 71.3% to 87.0% compared to a traditional vision-based approach using a single image convolutional neural network (CNN). Incorporating sensor data further increased the score to 90.1%. These results highlight the benefits of integrating temporal dependencies and multimodal inputs and demonstrate the potential for more effective driver distraction detection systems that can improve road safety. Full article
(This article belongs to the Special Issue AI-Driven Automotive Advances: From Passenger Monitoring to Autonomous Navigation)
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19 pages, 2586 KiB  
Article
Decision-Making in Fallback Scenarios for Autonomous Vehicles: Deep Reinforcement Learning Approach
by Cheonghwa Lee and Dawn An
Appl. Sci. 2023, 13(22), 12258; https://doi.org/10.3390/app132212258 - 13 Nov 2023
Viewed by 1629
Abstract
This paper proposes a decision-making algorithm based on deep reinforcement learning to support fallback techniques in autonomous vehicles. The fallback technique attempts to mitigate or escape risky driving conditions by responding to appropriate avoidance maneuvers essential for achieving a Level 4+ autonomous driving [...] Read more.
This paper proposes a decision-making algorithm based on deep reinforcement learning to support fallback techniques in autonomous vehicles. The fallback technique attempts to mitigate or escape risky driving conditions by responding to appropriate avoidance maneuvers essential for achieving a Level 4+ autonomous driving system. However, developing a fallback technique is difficult because of the innumerable fallback situations to address and eligible optimal decision-making among multiple maneuvers. We employed a decision-making algorithm utilizing a scenario-based learning approach to address these issues. First, we crafted a specific fallback scenario encompassing the challenges to be addressed and matched the anticipated optimal maneuvers as determined by heuristic methods. In this scenario, the ego vehicle learns through trial and error to determine the most effective maneuver. We conducted 100 independent training sessions to evaluate the proposed algorithm and compared the results with those of heuristic-derived maneuvers. The results were promising; 38% of the training sessions resulted in the vehicle learning lane-change maneuvers, whereas 9% mastered slow following. Thus, the proposed algorithm successfully learned human-equivalent fallback capabilities from scratch within the provided scenario. Full article
(This article belongs to the Special Issue AI-Driven Automotive Advances: From Passenger Monitoring to Autonomous Navigation)
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