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: 20 March 2024 | Viewed by 1286

Special Issue Editors

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
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
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

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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

Published Papers (1 paper)

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Research

19 pages, 2586 KiB  
Article
Decision-Making in Fallback Scenarios for Autonomous Vehicles: Deep Reinforcement Learning Approach
Appl. Sci. 2023, 13(22), 12258; https://doi.org/10.3390/app132212258 - 13 Nov 2023
Viewed by 824
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
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