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Sensors for Intelligent Vehicles and Autonomous Driving
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Sensors for Intelligent Vehicles and Autonomous Driving

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1866

Special Issue Editors

Dr. Xin Xia

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA
Interests: cooperative driving automation; state estimation; cooperative localization; cooperative perception; the dynamic control of connected autonomous vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Letian Gao

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA
Interests: vehicle localization; state estimation; the dynamic control of intelligent vehicles

Special Issue Information

Dear Colleagues,

The rapid development of intelligent vehicles and autonomous driving technologies has garnered significant attention from both academia and industry in recent years. A critical component in the development of these systems is the integration of various sensor technologies that enable vehicles to perceive and understand their surroundings, make informed decisions, and navigate safely in complex environments. The increasing demand for higher levels of autonomy and safety in modern transportation systems has spurred substantial research and innovations in sensor technologies for intelligent vehicles and autonomous driving.

The primary motivation behind this Special Issue is to address the growing need for a comprehensive understanding of the latest sensor technologies, their applications, and the challenges associated with their implementation in intelligent vehicles and autonomous driving systems. By collating experts from various disciplines, this Special Issue aims to foster interdisciplinary collaboration and drive innovation in the design, development, and deployment of sensor technologies for intelligent vehicles and autonomous driving.

Dr. Xin Xia
Dr. Letian Gao
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. Sensors 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 2600 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

  • vehicle localization and mapping
  • obstacle detection and avoidance
  • lane detection and tracking
  • traffic sign and signal recognition
  • pedestrian and cyclist detection
  • vehicle-to-everything (V2X) communication
  • driver monitoring and assistance systems
  • advanced driver assistance systems (ADASs)

Published Papers (2 papers)

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Research

17 pages, 13759 KiB  
Article
Explaining Bounding Boxes in Deep Object Detectors Using Post Hoc Methods for Autonomous Driving Systems
by Caio Nogueira, Luís Fernandes, João N. D. Fernandes and Jaime S. Cardoso
Sensors 2024, 24(2), 516; https://doi.org/10.3390/s24020516 - 14 Jan 2024
Viewed by 727
Abstract
Deep learning has rapidly increased in popularity, leading to the development of perception solutions for autonomous driving. The latter field leverages techniques developed for computer vision in other domains for accomplishing perception tasks such as object detection. However, the black-box nature of deep [...] Read more.
Deep learning has rapidly increased in popularity, leading to the development of perception solutions for autonomous driving. The latter field leverages techniques developed for computer vision in other domains for accomplishing perception tasks such as object detection. However, the black-box nature of deep neural models and the complexity of the autonomous driving context motivates the study of explainability in these models that perform perception tasks. Moreover, this work explores explainable AI techniques for the object detection task in the context of autonomous driving. An extensive and detailed comparison is carried out between gradient-based and perturbation-based methods (e.g., D-RISE). Moreover, several experimental setups are used with different backbone architectures and different datasets to observe the influence of these aspects in the explanations. All the techniques explored consist of saliency methods, making their interpretation and evaluation primarily visual. Nevertheless, numerical assessment methods are also used. Overall, D-RISE and guided backpropagation obtain more localized explanations. However, D-RISE highlights more meaningful regions, providing more human-understandable explanations. To the best of our knowledge, this is the first approach to obtaining explanations focusing on the regression of the bounding box coordinates. Full article
(This article belongs to the Special Issue Sensors for Intelligent Vehicles and Autonomous Driving)
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19 pages, 3459 KiB  
Article
Method of Evaluating Multiple Scenarios in a Single Simulation Run for Automated Vehicle Assessment
by Inyoung Kim, Donghyo Kang, Harim Jeong, Soomok Lee and Ilsoo Yun
Sensors 2023, 23(19), 8271; https://doi.org/10.3390/s23198271 - 06 Oct 2023
Viewed by 865
Abstract
With advances in the technology applied to automated driving systems (ADSs), active efforts have been made to evaluate the safety of ADS in various complex situations using simulations. In accordance with these efforts, numerous institutions have developed single-scenario pools that reflect a variety [...] Read more.
With advances in the technology applied to automated driving systems (ADSs), active efforts have been made to evaluate the safety of ADS in various complex situations using simulations. In accordance with these efforts, numerous institutions have developed single-scenario pools that reflect a variety of road and traffic characteristics and ADS performances. However, a single scenario has limitations in comprehensively evaluating the performance of complex ADS. Therefore, this study proposed a methodology that combines and transforms single scenarios into multiple scenarios. This aided in continuously evaluating the ADS performance over entire road segments and implemented this methodology in the simulations. Full article
(This article belongs to the Special Issue Sensors for Intelligent Vehicles and Autonomous Driving)
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