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Artificial Intelligence in Automotive Technology
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Artificial Intelligence in Automotive Technology

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 11113

Special Issue Editors

Prof. Dr. Markus Lienkamp

E-Mail Website
Guest Editor
Institute of Automotive Technology, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany
Interests: autonomous driving; future mobility; electric vehicles
Maximilian Geisslinger

E-Mail Website
Guest Editor Assistant
Institute of Automotive Technology, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany
Interests: autonomous driving; neural networks; trajectory planning
Felix Fent

E-Mail Website
Guest Editor Assistant
Institute of Automotive Technology, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Germany
Interests: autonomous driving; environmental perception; sensor fusion

Special Issue Information

Dear Colleagues,

 Autonomous vehicles (AVs) will have a global impact that will change society, roadway safety, and transportation systems in the future. They offer new opportunities to meet the ever-increasing demands in urban mobility and the modern logistics sector.

 The rise of machine learning enables new approaches to AV algorithms, bringing the first AVs on public roads in cities such as Phoenix and San Francisco. However, there are still significant challenges regarding robustness, for example, in complex scenarios (edge cases) or severe weather conditions, which are often approached by sensor fusion techniques. Additionally, transferring existing algorithms to new domains still requires vast amount of data, which are increasingly replaced by simulated or synthetic sensor data.

 The Special Issues covers all relevant aspects in the field of artificial intelligence, with a particular focus on machine learning and deep learning techniques. In addition, all theoretical aspects will be related to automotive technology topics in the context of sensors.

Prof. Dr. Markus Lienkamp
Maximilian Geisslinger
Felix Fent
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

  • artificial intelligence
  • machine learning
  • self-driving cars
  • environmental perception
  • sensor fusion
  • trajectory planning
  • simulation
  • neural networks
  • convolutional neural networks
  • reinforcement learning
  • automotive

Published Papers (4 papers)

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Research

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20 pages, 4777 KiB  
Article
Research into Autonomous Vehicles Following and Obstacle Avoidance Based on Deep Reinforcement Learning Method under Map Constraints
by Zheng Li, Shihua Yuan, Xufeng Yin, Xueyuan Li and Shouxing Tang
Sensors 2023, 23(2), 844; https://doi.org/10.3390/s23020844 - 11 Jan 2023
Cited by 4 | Viewed by 3911
Abstract
Compared with traditional rule-based algorithms, deep reinforcement learning methods in autonomous driving are able to reduce the response time of vehicles to the driving environment and fully exploit the advantages of autopilot. Nowadays, autonomous vehicles mainly drive on urban roads and are constrained [...] Read more.
Compared with traditional rule-based algorithms, deep reinforcement learning methods in autonomous driving are able to reduce the response time of vehicles to the driving environment and fully exploit the advantages of autopilot. Nowadays, autonomous vehicles mainly drive on urban roads and are constrained by some map elements such as lane boundaries, lane driving rules, and lane center lines. In this paper, a deep reinforcement learning approach seriously considering map elements is proposed to deal with the autonomous driving issues of vehicles following and obstacle avoidance. When the deep reinforcement learning method is modeled, an obstacle representation method is proposed to represent the external obstacle information required by the ego vehicle input, aiming to address the problem that the number and state of external obstacles are not fixed. Full article
(This article belongs to the Special Issue Artificial Intelligence in Automotive Technology)
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21 pages, 3427 KiB  
Article
Interactive Lane Keeping System for Autonomous Vehicles Using LSTM-RNN Considering Driving Environments
by Yonghwan Jeong
Sensors 2022, 22(24), 9889; https://doi.org/10.3390/s22249889 - 15 Dec 2022
Cited by 3 | Viewed by 2701
Abstract
This paper presents an interactive lane keeping model for an advanced driver assistant system and autonomous vehicle. The proposed model considers not only the lane markers but also the interaction with surrounding vehicles in determining steering inputs. The proposed algorithm is designed based [...] Read more.
This paper presents an interactive lane keeping model for an advanced driver assistant system and autonomous vehicle. The proposed model considers not only the lane markers but also the interaction with surrounding vehicles in determining steering inputs. The proposed algorithm is designed based on the Recurrent Neural Network (RNN) with long short-term memory cells, which are configured by the collected driving data. A data collection vehicle is equipped with a front camera, LiDAR, and DGPS. The input features of the RNN consist of lane information, surrounding targets, and ego vehicle states. The output feature is the steering wheel angle to keep the lane. The proposed algorithm is evaluated through similarity analysis and a case study with driving data. The proposed algorithm shows accurate results compared to the conventional algorithm, which only considers the lane markers. In addition, the proposed algorithm effectively responds to the surrounding targets by considering the interaction with the ego vehicle. Full article
(This article belongs to the Special Issue Artificial Intelligence in Automotive Technology)
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28 pages, 25800 KiB  
Article
Noise-Adaptive Non-Blind Image Deblurring
by Michael Slutsky
Sensors 2022, 22(18), 6923; https://doi.org/10.3390/s22186923 - 13 Sep 2022
Cited by 3 | Viewed by 1792
Abstract
This work addresses the problem of non-blind image deblurring for arbitrary input noise. The problem arises in the context of sensors with strong chromatic aberrations, as well as in standard cameras, in low-light and high-speed scenarios. A short description of two common classical [...] Read more.
This work addresses the problem of non-blind image deblurring for arbitrary input noise. The problem arises in the context of sensors with strong chromatic aberrations, as well as in standard cameras, in low-light and high-speed scenarios. A short description of two common classical approaches to regularized image deconvolution is provided, and common issues arising in this context are described. It is shown how a pre-deconvolved deep neural network (DNN) based image enhancement can be improved by joint optimization of regularization parameters and network weights. Furthermore, a two-step approach to deblurring based on two DNNs is proposed, with the first network estimating deconvolution regularization parameters, and the second one performing image enhancement and residual artifact removal. For the first network, a novel RegParamNet architecture is introduced and its performance is examined for both direct and indirect regularization parameter estimation. The system is shown to operate well for input noise in a three orders of magnitude range (0.01–10.0) and a wide spectrum of 1D or 2D Gaussian blur kernels, well outside the scope of most previously explored image blur and noise degrees. The proposed method is found to significantly outperform several leading state-of-the-art approaches. Full article
(This article belongs to the Special Issue Artificial Intelligence in Automotive Technology)
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Review

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19 pages, 1543 KiB  
Review
The Use of Terrestrial and Maritime Autonomous Vehicles in Nonintrusive Object Inspection
by Dmytro Mamchur, Janis Peksa, Antons Kolodinskis and Maksims Zigunovs
Sensors 2022, 22(20), 7914; https://doi.org/10.3390/s22207914 - 18 Oct 2022
Cited by 6 | Viewed by 1500
Abstract
Traditional nonintrusive object inspection methods are complex or extremely expensive to apply in certain cases, such as inspection of enormous objects, underwater or maritime inspection, an unobtrusive inspection of a crowded place, etc. With the latest advances in robotics, autonomous self-driving vehicles could [...] Read more.
Traditional nonintrusive object inspection methods are complex or extremely expensive to apply in certain cases, such as inspection of enormous objects, underwater or maritime inspection, an unobtrusive inspection of a crowded place, etc. With the latest advances in robotics, autonomous self-driving vehicles could be applied for this task. The present study is devoted to a review of the existing and novel technologies and methods of using autonomous self-driving vehicles for nonintrusive object inspection. Both terrestrial and maritime self-driving vehicles, their typical construction, sets of sensors, and software algorithms used for implementing self-driving motion were analyzed. The standard types of sensors used for nonintrusive object inspection in security checks at the control points, which could be successfully implemented at self-driving vehicles, along with typical areas of implementation of such vehicles, were reviewed, analyzed, and classified. Full article
(This article belongs to the Special Issue Artificial Intelligence in Automotive Technology)
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